diff --git a/README.md b/README.md index 36c81e3..8cfc030 100644 --- a/README.md +++ b/README.md @@ -254,6 +254,40 @@ Notes: - All aggregators operate over per‑observation scores where non‑confident observations are already clipped to 0. - The default `skewness` remains a good choice when user activity volume varies widely. +## Simulation-based tuning + +Which aggregator and hyperparameters work best depends on your data, so it helps to measure them on labeled examples. The `sentinel.simulation` module is a small, numpy‑only harness for exactly that — no Ray, S3, or experiment trackers required. + +Give it groups of observations with known labels (`1` = rare/positive source, `0` = common/negative source), score them once, then compare every aggregator (and hyperparameter) cheaply: + +```python +import pandas as pd +from sentinel.simulation import LabeledGroup, score_groups, compare_aggregators, run_grid_search + +groups = [ + LabeledGroup(name="source_a", label=1, observations=["...", "..."]), # known rare-class source + LabeledGroup(name="source_b", label=0, observations=["...", "..."]), # known common-class source + # ... +] + +# Expensive step (runs the model) — done once. +scored = score_groups(index, groups, top_k=5) + +# Cheap step — compare all six aggregators on the same scores. +pd.DataFrame(compare_aggregators(scored)) + +# Or sweep hyperparameters as well. +pd.DataFrame(run_grid_search(index, groups, top_k_values=[3, 5, 10], min_score_values=[0.0, 0.1, 0.25])) +``` + +Each result row reports three families of evaluation metrics, so you can tune for whatever matters to your use case: + +- Ranking: `roc_auc`, `recall_at_n`, `precision_at_n`, `rank_ratio` (do known positives rank at the top?) +- Threshold / classification: `precision`, `recall`, `f1`, `false_positive_rate` at a chosen (or automatically best‑F1) cutoff +- Separation / distribution: `mean_separation`, `cohens_d`, `ks_statistic` (threshold‑free) + +See [examples/sentinel_against_hate.ipynb](examples/sentinel_against_hate.ipynb) for a worked example comparing aggregators on hate‑speech data. + ## How It Works Sentinel uses a two-step process to detect rare classes of text, focusing on high recall for realtime applications: diff --git a/docs/source/modules.rst b/docs/source/modules.rst index 0859667..82a26d7 100644 --- a/docs/source/modules.rst +++ b/docs/source/modules.rst @@ -5,3 +5,4 @@ API Reference :maxdepth: 4 sentinel + diff --git a/docs/source/sentinel.rst b/docs/source/sentinel.rst index b0f78d7..398fbf5 100644 --- a/docs/source/sentinel.rst +++ b/docs/source/sentinel.rst @@ -34,6 +34,14 @@ sentinel.sentinel_local_index :undoc-members: :show-inheritance: +sentinel.simulation +------------------- + +.. automodule:: sentinel.simulation + :members: + :undoc-members: + :show-inheritance: + Subpackages ---------- diff --git a/examples/Example_Threshold_Script.py b/examples/Example_Threshold_Script.py index 3f9d498..c085eb3 100644 --- a/examples/Example_Threshold_Script.py +++ b/examples/Example_Threshold_Script.py @@ -33,6 +33,11 @@ import time from typing import Dict, List +# Path to a saved Sentinel index. By default this points at the hate-speech +# index built by examples/sentinel_against_hate.ipynb. Change it to your own +# index path (local directory or s3:// URI) if needed. +DEFAULT_INDEX_PATH = "./hate_speech_model" + def create_user_profiles() -> Dict[str, List[str]]: """Create 10 different user profiles with varying speech patterns.""" @@ -163,9 +168,9 @@ def test_thresholds_and_ratios(review_mode: bool = False, # Time data loading load_start = time.time() index = SentinelLocalIndex.load( - path="path/to/local/index", + path=DEFAULT_INDEX_PATH, negative_to_positive_ratio=ratio, - Cache_Model=True & ~no_cache + cache_model=not no_cache, ) load_time = time.time() - load_start total_load_time += load_time diff --git a/examples/sentinel_against_hate.ipynb b/examples/sentinel_against_hate.ipynb index bdf9338..5936edc 100644 --- a/examples/sentinel_against_hate.ipynb +++ b/examples/sentinel_against_hate.ipynb @@ -113,20 +113,6 @@ "Found 249 markdown files.\n" ] }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6ff47bdd94334db8970505b6b189dc05", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing markdown files: 0%| | 0/249 [00:00\n", " \n", " \n", - " 617\n", - " center-immigration-studies\n", - " Washington Times\n", + " 51\n", + " richard-bertrand-spencer\n", + " “We’re going to be back here, and we’re going to humiliate all of these people who opposed us. We’ll be back here 1,000 times if necessary. I always win. Because I have the will to win, I keep going until I win.”\n", " \n", " \n", - " 927\n", - " louis-beam\n", - " — “New World Order,” 1999 essay by Beam\n", + " 168\n", + " tim-wildmon\n", + " “[Islam] is in fact a religion of war, violence, intolerance, and physical persecution of non-Muslims.”\n", " \n", " \n", - " 942\n", - " bill-white\n", - " In August 2004, White was convicted of assaulting a woman when she was handing out flyers that identified him as a neo-Nazi landlord. During the court proceedings, White cursed at the woman from the witness stand and was held in contempt of court by the judge. In December 2009, a federal jury found White guilty of threatening several people through intimidating phone calls and internet postings. He was sentenced to 2 ½ years in prison at an April 2010 hearing. In January 2011, White was found guilty of using his website to encourage violence against a jury foreman in the trial of another white supremacist,\n", + " 2468\n", + " misogynist-incels\n", + " “Jews heavily pushed feminism. Roasties have such high standards because they are ‘liberated’. You probably would have gotten your d*** wet by now if it weren’t for feminism and for Jews.”\n", " \n", " \n", - " 973\n", - " william-pierce\n", - " — Pierce, quoted in a biography of him called Fame of a Dead Man’s Deeds, on the power of narrative\n", + " 1040\n", + " kyle-bristow\n", + " — Quoted in the\n", " \n", " \n", - " 1967\n", - " tucker-carlson\n", - " a reporter later exposed\n", + " 422\n", + " raymond-cattell\n", + " “Suppose, as may well be the case, that one of these races is naturally courageous, self-sacrificing and enterprising and the other less so. The group will continue to prosper owing to the activities of inventors and explorers of the first race, who, as is generally the rule, will not pass on the usual number of children to the next generation. The nation will be successful in war because the same race has actively responded to the call to arms and to self-sacrifice. Throughout these activities, this first race will on an average be giving more to the group than it can itself recoup. Eventually only the second race will inherit the group advantages acquired largely by the first racial compound. Then like a huge parasite which has devoured its host, will the nation be bereft of all the qualities that gave it power, remain a monstrous frustration of evolution, a biological abortion able in virtue of its inherited wealth, to do untold damage to neighboring races naturally more capable. The hatred and abhorrence which many peoples feel for the Jewish (and to some extent Mongolian) practice of living in other nations, instead of forming an independent, self-sustained group of their own, comes from a deep intuitive feeling that somehow it is not ‘playing the game.’ Because our unbiologically-minded civilization cannot perceive or appreciate any intellectual causes for these feelings they are readily branded as ‘prejudice’ by would-be intellectuals.”\n", " \n", " \n", - " 1116\n", - " stefan-molyneux\n", - " Collective Guilt for Fun and Profit”,\n", + " 1479\n", + " joseph-francis-farah\n", + " Polygamy? Sure.\n", " \n", " \n", - " 1375\n", - " malik-zulu-shabazz\n", - " Audience: “Jews!”\n", + " 585\n", + " bill-white\n", + " — Headline for 2008 article about then-presidential candidate Barack Obama who was depicted in the middle of a rifle scope on the cover of White’s American National Socialist Workers Party’s magazine\n", " \n", " \n", - " 170\n", - " paul-nehlen\n", - " Nehlen’s 2016 campaign for Congress was ideologically similar to Trump’s and was buoyed by FOX News and other outlets with large, national audiences. Breitbart.com ran several stories on Nehlen, for example. Right-wing pundits such as\n", + " 220\n", + " lydia-brimelow\n", + " “As the President of the VDARE Foundation, I work alongside my husband, Editor of VDARE.com, Peter Brimelow, to defeat the post-1964 Immigration Act disaster that has set America on a course of destruction.”\n", " \n", " \n", - " 2025\n", - " tucker-carlson\n", - " “Tucker is ultimately on our side. He can get millions and millions of boomers to nod along with talking points that would have only been seen on VDare or American Renaissance a few years ago,” Greer said on his podcast in the Spring of 2021,\n", + " 2467\n", + " misogynist-incels\n", + " – Comment on an incel forum post titled “‘They are animals…’ ER was right,”\n", " \n", " \n", - " 321\n", - " ronald-doggett\n", - " “Here in the state of Virginia our Confederate History Month proclamations became so watered down with nods towards the non-Whites they weren’t even worth supporting. We can’t ever have anything just for Whites…I wonder what a true Confederate (White Supremacist) brought to today’s time would think of the cowardly defenders of his cause.”\n", + " 2439\n", + " james-lindsay\n", + " If anyone causes one of these little ones—those who believe in me—to stumble, it would be better for them to have a large millstone hung around their neck and to be drowned in the depths of the sea.”)\n", " \n", " \n", - " 1000\n", - " scott-lively\n", - " “It is not mere coincidence that the emperors of Rome in its horrific final days were homosexual; that Adolf Hitler’s inner circle were mostly homosexual; and that nearly all of the most prolific serial killers in U.S. history were homosexual. It is not mere coincidence that America’s cultural decline parallels the rise of ‘gay rights.’”\n", + " 1535\n", + " jeff-berry\n", + " — 1998 speech at a Klan rally in Jasper, Texas, after the truck-dragging murder there of James Byrd Jr.\n", " \n", " \n", - " 2331\n", - " proud-boys\n", - " “I am not afraid to speak out about the atrocities that whites and people of European descent face not only here in this country but in Western nations across the world. The war against whites, and Europeans and Western society is very real and it’s time we all started talking about it and stopped worrying about political correctness and optics.” – Kyle Chapman, who formed the Fraternal Order of Alt-Knights, a paramilitary wing of the Proud Boys, Unite America First Peace Rally, Sacramento, California, July 8, 2017\n", + " 49\n", + " harry-cooper\n", + " “We must remember that official government sources state definitely that Hitler committed suicide in the bunker and that the body found not far from the Chancellery was indeed that of Martin Bormann. … Naturally we must believe what the governments tell us, like John F. Kennedy was shot by one madman using a worthless rifle when nobody with any sense buys that story except the Warren Commission. … The list of lies by the world’s governments could by themselves, fill a major size book but you already know that.”\n", " \n", " \n", - " 408\n", - " moms-liberty\n", - " “Gender dysphoria is a mental health disorder that is being normalized by predators across the USA. California kids are at extreme risk from predatory adults. Now they want to ‘liberate’ children all over the country. Does a double mastectomy on a preteen sound like progress?’\n", + " 535\n", + " david-irving\n", + " — 1991 speech to a Canadian audience\n", " \n", " \n", - " 651\n", - " center-immigration-studies\n", - " American Renaissance\n", + " 175\n", + " jack-posobiec\n", + " “StoptheSteal 2020 is coming.” – Twitter, Sept. 7, 2020, a full two months before news outlets declared Joe Biden the winner of the 2020 presidential election\n", " \n", " \n", - " 1614\n", - " tomislav-sunic\n", - " Postmortem Report: Cultural Examinations from Postmodernity\n", + " 1464\n", + " willis-carto\n", + " The Barnes Review\n", " \n", " \n", - " 1135\n", - " stefan-molyneux\n", - " “If we could just get people to be nice to their babies for five years straight, that would be it for war, drug abuse, addiction, promiscuity, sexually transmitted diseases. Almost all would be completely eliminated, because they all arise from dysfunctional early childhood experiences, which are all run by women.”\n", + " 405\n", + " proud-boys\n", + " “All the heroes of BLM and Antifa are degenerate criminal lowlifes or pedophile rapists. I don’t lose any sleep when they are justly removed from society.” – A Telegram channel associated with the Proud Boys, Sept. 22, 2020\n", " \n", " \n", - " 582\n", - " center-immigration-studies\n", - " (CCC), which Charleston shooter\n", + " 846\n", + " greg-johnson\n", + " — “Dealing with the Holocaust\n", " \n", " \n", - " 1961\n", - " tucker-carlson\n", - " served as a platform\n", + " 598\n", + " atomwaffen-division\n", + " “Pro f—– propaganda is working! AIDS is spreading like wildfire. Dead f—— couldn’t make us happier! Hail AIDS!” – Atomwaffen Division website\n", " \n", " \n", - " 1640\n", - " michael-flynn\n", - " “We the people are proud to proclaim that the United States of America is ‘One Nation under God’ – in this public profession of faith in God, we recognize his Lordship over our country, and we proudly stand beneath the banner of Christ and our flag in which millions have sacrificed their very lives for. In scripture through the strength and commitment of Matthew, he said, ‘Whoever is not with Me is against Me.’” – Blog post titled\n", + " 2493\n", + " mark-weber\n", + " “Around the world awareness is growing that the ‘Holocaust’ campaign is a major weapon in the Jewish-Zionist arsenal, that it is used to justify otherwise unjustifiable Israeli policies, and as a powerful tool for blackmailing enormous sums of money from Americans and Europeans.”\n", " \n", " \n", - " 1073\n", - " paul-elam\n", - " “P—- is the only real empowerment women will ever know. Put all the hopelessly wishful thinking of feminist ideology aside and what remains is the fact that it is men, and pretty much men only, who draw power from accomplishment, who invent technology, build nations, cure disease, create empires and generally advance civilization. Women – whether acknowledging it makes us feel warm and fuzzy or not – depend on men for all of that and the only tool they have at their disposal to have any sort of influence on any of it is the power of p—- and p—- is powerful indeed…Sexual robotics may well prove to be the best thing that ever happened to women from the standpoint of their humanity…. what would that do to the vast majority of women who would suddenly have to prove their worth as human beings beyond simply being the owners of said p—-?” – Paul Elam, An Ear for Men, Sex Robots: Part 3 – Disempowering P—-, October 2017\n", + " 1052\n", + " gays-against-groomers\n", + " “The modern trans movement is radicalizing activists into terrorists. […] Trans terrorism is on the rise. […] This sends a message: if you don’t let us mutilate children’s genitals, then we will shoot yours.” – GAG then-Secretary David Leatherwood, “Real America with Dan Ball,” in response to the Covenant School shooting in Nashville, Tennessee,\n", " \n", " \n", - " 1582\n", - " mike-cernovich\n", - " Despite making completely unsubstantiated accusations of pedophilia, Cernovich discussed the allegations that aspiring Alabama senator Roy Moore had sexually abused underage girls cautiously. After tweeting that “If it’s true, string the guy up, man. I got no problem with that,” he later retweeted individuals casting doubt on the validity of the accusers, writing: “When you’re lied about in the news daily, as I am, you pause when 40-year-old accusations surface one month away from an election where WaPo endorsed the other candidate.” Taking it further, however, Cernovich then recorded a podcast giving “scientific” advice to Roy Moore,\n", + " 2485\n", + " john-de-nugent\n", + " “They [the Jews] control the media and so it spews lies against the enemy they fear most. And the Jews now misrule our beloved country of America, bringing it down by huge bribes, media brainwashing and blackmail of our officials, and police, and military officers both high and low. And worst of all, by a national network of organized pedophiles, child molesting and child-murdering adults who once involved in that network, one orgy at a time, are photographed there, captured forever on film. That film is stored away for future blackmail use. Those officials cannot quit even if they want to.”\n", " \n", " \n", - " 907\n", - " nation-islam\n", - " “Pedophilia and sexual perversion institutionalized in Hollywood and the entertainment industries can be traced to Talmudic principles and Jewish influence. Now Jewish influence – satanic influence under the name of Jew…The wicked practices that govern their industries are largely justified and influenced by such Talmudic principles. The pervasive rape culture, Hollywood’s casting couch, sex trafficking and prostitution, the age-old buck breaking process that emasculates Black men and corrupts Black women.”\n", + " 203\n", + " andrew-anglin\n", + " “Look, I hate women. I think they deserve to be beaten, raped and locked in cages.” – Daily Stomer, July 2018\n", " \n", " \n", - " 613\n", - " center-immigration-studies\n", - " No such reprimand occurred when Steinlight,\n", + " 1271\n", + " lieutenant-general-william-g-jerry-boykin-ret\n", + " “If we don’t take a stand here, it’s only a matter of time before biological men will be able to share showers and locker rooms with women and girls all across America! … Many on the Left believe that biological men have a right to share showers, locker rooms, and bathrooms with women and little girls as long as they identify as female at that particular point in time — and that anyone who disagrees with that should be punished!”\n", " \n", " \n", - " 2039\n", - " tucker-carlson\n", - " the digital publication.\n", + " 297\n", + " alt-right\n", + " “Immigration is a kind of proxy war—and maybe a last stand—for White Americans, who are undergoing a painful recognition that, unless dramatic action is taken, their grandchildren will live in a country that is alien and hostile.”\n", " \n", " \n", - " 962\n", - " edgar-steele\n", - " “Without a pressure release valve, as open racism once provided, an explosion of epic proportions at some time in the future is guaranteed. There will be a race war, the initial skirmishes of which already are being fought in America’s streets, that will bring an altogether new meaning to the concepts of race war and genocide, courtesy of those who claim to abhor racism.”\n", + " 453\n", + " garrett-hardin\n", + " —“How Diversity Should be Nurtured,”\n", " \n", " \n", - " 2060\n", - " tucker-carlson\n", - " Tucker Carlson hosted male supremacist Andrew Tate on August 5, 2022. (Twitter)\n", + " 367\n", + " alex-jones\n", + " “Imagine how bad she [Hillary Clinton] smells, man? I’m told her and Obama, just stink, stink, stink, stink. You can’t wash that evil off, man. Told there’s a rotten smell around Hillary. I’m not kidding, people say, they say — folks, I’ve been told this by high-up folks. They say listen, Obama and Hillary both smell like sulfur. … I’ve talked to people that are in protective details, they’re scared of her. And they say listen, she’s a frickin’ demon and she stinks and so does Obama. I go, like what? Sulfur. They smell like hell.” — “The Alex Jones Show,” Oct. 10, 2016\n", " \n", " \n", - " 1627\n", - " james-timothy-turner\n", - " “I am not in jail for violating the law. I am in jail because I stood for righteousness and truth in government. Pray that God will crumble the foundations and break the power and strength of the corporation and restore his righteous government in America.”\n", + " 1055\n", + " gays-against-groomers\n", + " “Every year, THOUSANDS of children are exposed to displays of sexual depravity at Pride events, and MILLIONS applaud it. This year, Gays Against Groomers will not stand for this destruction of childhood innocence. Be seeing you REAL soon.” – GAG Illinois X (formerly Twitter),\n", " \n", " \n", - " 2186\n", - " matt-walsh\n", - " In addition to spreading disinformation about transgender identity and discredited pseudoscience, Walsh’s film relies on propagandist tactics of narrative manipulation in suggesting legal protections for transgender people will lead to people being prosecuted for using the wrong pronouns. And in making numerous\n", + " 237\n", + " chuck-baldwin\n", + " “I believe homosexuality is moral perversion and deserves no special consideration under the law. … I believe the South was right in the War Between the States, and I am not a racist.”\n", " \n", " \n", - " 866\n", - " barry-black\n", - " — A 1998 comment to a Virginia newspaper\n", + " 1553\n", + " craig-cobb\n", + " —Nov. 16, 2013, videotaped rant against a resident while “patrolling” the streets of Leith\n", " \n", " \n", - " 471\n", - " david-yerushalmi\n", - " — “Offensive and Defensive Lawfare: Fighting Civilization Jihad in America’s Courts,” Center for Security Policy press release,\n", + " 677\n", + " family-research-council\n", + " “The reality is, homosexuals have entered the Scouts in the past for predatory purposes.”\n", " \n", " \n", "\n", "" ], "text/plain": [ - " filename \\\n", - "617 center-immigration-studies \n", - "927 louis-beam \n", - "942 bill-white \n", - "973 william-pierce \n", - "1967 tucker-carlson \n", - "1116 stefan-molyneux \n", - "1375 malik-zulu-shabazz \n", - "170 paul-nehlen \n", - "2025 tucker-carlson \n", - "321 ronald-doggett \n", - "1000 scott-lively \n", - "2331 proud-boys \n", - "408 moms-liberty \n", - "651 center-immigration-studies \n", - "1614 tomislav-sunic \n", - "1135 stefan-molyneux \n", - "582 center-immigration-studies \n", - "1961 tucker-carlson \n", - "1640 michael-flynn \n", - "1073 paul-elam \n", - "1582 mike-cernovich \n", - "907 nation-islam \n", - "613 center-immigration-studies \n", - "2039 tucker-carlson \n", - "962 edgar-steele \n", - "2060 tucker-carlson \n", - "1627 james-timothy-turner \n", - "2186 matt-walsh \n", - "866 barry-black \n", - "471 david-yerushalmi \n", + " filename \\\n", + "51 richard-bertrand-spencer \n", + "168 tim-wildmon \n", + "2468 misogynist-incels \n", + "1040 kyle-bristow \n", + "422 raymond-cattell \n", + "1479 joseph-francis-farah \n", + "585 bill-white \n", + "220 lydia-brimelow \n", + "2467 misogynist-incels \n", + "2439 james-lindsay \n", + "1535 jeff-berry \n", + "49 harry-cooper \n", + "535 david-irving \n", + "175 jack-posobiec \n", + "1464 willis-carto \n", + "405 proud-boys \n", + "846 greg-johnson \n", + "598 atomwaffen-division \n", + "2493 mark-weber \n", + "1052 gays-against-groomers \n", + "2485 john-de-nugent \n", + "203 andrew-anglin \n", + "1271 lieutenant-general-william-g-jerry-boykin-ret \n", + "297 alt-right \n", + "453 garrett-hardin \n", + "367 alex-jones \n", + "1055 gays-against-groomers \n", + "237 chuck-baldwin \n", + "1553 craig-cobb \n", + "677 family-research-council \n", "\n", - " paragraph \n", - "617 Washington Times \n", - "927 — “New World Order,” 1999 essay by Beam \n", - "942 In August 2004, White was convicted of assaulting a woman when she was handing out flyers that identified him as a neo-Nazi landlord. During the court proceedings, White cursed at the woman from the witness stand and was held in contempt of court by the judge. In December 2009, a federal jury found White guilty of threatening several people through intimidating phone calls and internet postings. He was sentenced to 2 ½ years in prison at an April 2010 hearing. In January 2011, White was found guilty of using his website to encourage violence against a jury foreman in the trial of another white supremacist, \n", - "973 — Pierce, quoted in a biography of him called Fame of a Dead Man’s Deeds, on the power of narrative \n", - "1967 a reporter later exposed \n", - "1116 Collective Guilt for Fun and Profit”, \n", - "1375 Audience: “Jews!” \n", - "170 Nehlen’s 2016 campaign for Congress was ideologically similar to Trump’s and was buoyed by FOX News and other outlets with large, national audiences. Breitbart.com ran several stories on Nehlen, for example. Right-wing pundits such as \n", - "2025 “Tucker is ultimately on our side. He can get millions and millions of boomers to nod along with talking points that would have only been seen on VDare or American Renaissance a few years ago,” Greer said on his podcast in the Spring of 2021, \n", - "321 “Here in the state of Virginia our Confederate History Month proclamations became so watered down with nods towards the non-Whites they weren’t even worth supporting. We can’t ever have anything just for Whites…I wonder what a true Confederate (White Supremacist) brought to today’s time would think of the cowardly defenders of his cause.” \n", - "1000 “It is not mere coincidence that the emperors of Rome in its horrific final days were homosexual; that Adolf Hitler’s inner circle were mostly homosexual; and that nearly all of the most prolific serial killers in U.S. history were homosexual. It is not mere coincidence that America’s cultural decline parallels the rise of ‘gay rights.’” \n", - "2331 “I am not afraid to speak out about the atrocities that whites and people of European descent face not only here in this country but in Western nations across the world. The war against whites, and Europeans and Western society is very real and it’s time we all started talking about it and stopped worrying about political correctness and optics.” – Kyle Chapman, who formed the Fraternal Order of Alt-Knights, a paramilitary wing of the Proud Boys, Unite America First Peace Rally, Sacramento, California, July 8, 2017 \n", - "408 “Gender dysphoria is a mental health disorder that is being normalized by predators across the USA. California kids are at extreme risk from predatory adults. Now they want to ‘liberate’ children all over the country. Does a double mastectomy on a preteen sound like progress?’ \n", - "651 American Renaissance \n", - "1614 Postmortem Report: Cultural Examinations from Postmodernity \n", - "1135 “If we could just get people to be nice to their babies for five years straight, that would be it for war, drug abuse, addiction, promiscuity, sexually transmitted diseases. Almost all would be completely eliminated, because they all arise from dysfunctional early childhood experiences, which are all run by women.” \n", - "582 (CCC), which Charleston shooter \n", - "1961 served as a platform \n", - "1640 “We the people are proud to proclaim that the United States of America is ‘One Nation under God’ – in this public profession of faith in God, we recognize his Lordship over our country, and we proudly stand beneath the banner of Christ and our flag in which millions have sacrificed their very lives for. In scripture through the strength and commitment of Matthew, he said, ‘Whoever is not with Me is against Me.’” – Blog post titled \n", - "1073 “P—- is the only real empowerment women will ever know. Put all the hopelessly wishful thinking of feminist ideology aside and what remains is the fact that it is men, and pretty much men only, who draw power from accomplishment, who invent technology, build nations, cure disease, create empires and generally advance civilization. Women – whether acknowledging it makes us feel warm and fuzzy or not – depend on men for all of that and the only tool they have at their disposal to have any sort of influence on any of it is the power of p—- and p—- is powerful indeed…Sexual robotics may well prove to be the best thing that ever happened to women from the standpoint of their humanity…. what would that do to the vast majority of women who would suddenly have to prove their worth as human beings beyond simply being the owners of said p—-?” – Paul Elam, An Ear for Men, Sex Robots: Part 3 – Disempowering P—-, October 2017 \n", - "1582 Despite making completely unsubstantiated accusations of pedophilia, Cernovich discussed the allegations that aspiring Alabama senator Roy Moore had sexually abused underage girls cautiously. After tweeting that “If it’s true, string the guy up, man. I got no problem with that,” he later retweeted individuals casting doubt on the validity of the accusers, writing: “When you’re lied about in the news daily, as I am, you pause when 40-year-old accusations surface one month away from an election where WaPo endorsed the other candidate.” Taking it further, however, Cernovich then recorded a podcast giving “scientific” advice to Roy Moore, \n", - "907 “Pedophilia and sexual perversion institutionalized in Hollywood and the entertainment industries can be traced to Talmudic principles and Jewish influence. Now Jewish influence – satanic influence under the name of Jew…The wicked practices that govern their industries are largely justified and influenced by such Talmudic principles. The pervasive rape culture, Hollywood’s casting couch, sex trafficking and prostitution, the age-old buck breaking process that emasculates Black men and corrupts Black women.” \n", - "613 No such reprimand occurred when Steinlight, \n", - "2039 the digital publication. \n", - "962 “Without a pressure release valve, as open racism once provided, an explosion of epic proportions at some time in the future is guaranteed. There will be a race war, the initial skirmishes of which already are being fought in America’s streets, that will bring an altogether new meaning to the concepts of race war and genocide, courtesy of those who claim to abhor racism.” \n", - "2060 Tucker Carlson hosted male supremacist Andrew Tate on August 5, 2022. (Twitter) \n", - "1627 “I am not in jail for violating the law. I am in jail because I stood for righteousness and truth in government. Pray that God will crumble the foundations and break the power and strength of the corporation and restore his righteous government in America.” \n", - "2186 In addition to spreading disinformation about transgender identity and discredited pseudoscience, Walsh’s film relies on propagandist tactics of narrative manipulation in suggesting legal protections for transgender people will lead to people being prosecuted for using the wrong pronouns. And in making numerous \n", - "866 — A 1998 comment to a Virginia newspaper \n", - "471 — “Offensive and Defensive Lawfare: Fighting Civilization Jihad in America’s Courts,” Center for Security Policy press release, " + " paragraph \n", + "51 “We’re going to be back here, and we’re going to humiliate all of these people who opposed us. We’ll be back here 1,000 times if necessary. I always win. Because I have the will to win, I keep going until I win.” \n", + "168 “[Islam] is in fact a religion of war, violence, intolerance, and physical persecution of non-Muslims.” \n", + "2468 “Jews heavily pushed feminism. Roasties have such high standards because they are ‘liberated’. You probably would have gotten your d*** wet by now if it weren’t for feminism and for Jews.” \n", + "1040 — Quoted in the \n", + "422 “Suppose, as may well be the case, that one of these races is naturally courageous, self-sacrificing and enterprising and the other less so. The group will continue to prosper owing to the activities of inventors and explorers of the first race, who, as is generally the rule, will not pass on the usual number of children to the next generation. The nation will be successful in war because the same race has actively responded to the call to arms and to self-sacrifice. Throughout these activities, this first race will on an average be giving more to the group than it can itself recoup. Eventually only the second race will inherit the group advantages acquired largely by the first racial compound. Then like a huge parasite which has devoured its host, will the nation be bereft of all the qualities that gave it power, remain a monstrous frustration of evolution, a biological abortion able in virtue of its inherited wealth, to do untold damage to neighboring races naturally more capable. The hatred and abhorrence which many peoples feel for the Jewish (and to some extent Mongolian) practice of living in other nations, instead of forming an independent, self-sustained group of their own, comes from a deep intuitive feeling that somehow it is not ‘playing the game.’ Because our unbiologically-minded civilization cannot perceive or appreciate any intellectual causes for these feelings they are readily branded as ‘prejudice’ by would-be intellectuals.” \n", + "1479 Polygamy? Sure. \n", + "585 — Headline for 2008 article about then-presidential candidate Barack Obama who was depicted in the middle of a rifle scope on the cover of White’s American National Socialist Workers Party’s magazine \n", + "220 “As the President of the VDARE Foundation, I work alongside my husband, Editor of VDARE.com, Peter Brimelow, to defeat the post-1964 Immigration Act disaster that has set America on a course of destruction.” \n", + "2467 – Comment on an incel forum post titled “‘They are animals…’ ER was right,” \n", + "2439 If anyone causes one of these little ones—those who believe in me—to stumble, it would be better for them to have a large millstone hung around their neck and to be drowned in the depths of the sea.”) \n", + "1535 — 1998 speech at a Klan rally in Jasper, Texas, after the truck-dragging murder there of James Byrd Jr. \n", + "49 “We must remember that official government sources state definitely that Hitler committed suicide in the bunker and that the body found not far from the Chancellery was indeed that of Martin Bormann. … Naturally we must believe what the governments tell us, like John F. Kennedy was shot by one madman using a worthless rifle when nobody with any sense buys that story except the Warren Commission. … The list of lies by the world’s governments could by themselves, fill a major size book but you already know that.” \n", + "535 — 1991 speech to a Canadian audience \n", + "175 “StoptheSteal 2020 is coming.” – Twitter, Sept. 7, 2020, a full two months before news outlets declared Joe Biden the winner of the 2020 presidential election \n", + "1464 The Barnes Review \n", + "405 “All the heroes of BLM and Antifa are degenerate criminal lowlifes or pedophile rapists. I don’t lose any sleep when they are justly removed from society.” – A Telegram channel associated with the Proud Boys, Sept. 22, 2020 \n", + "846 — “Dealing with the Holocaust \n", + "598 “Pro f—– propaganda is working! AIDS is spreading like wildfire. Dead f—— couldn’t make us happier! Hail AIDS!” – Atomwaffen Division website \n", + "2493 “Around the world awareness is growing that the ‘Holocaust’ campaign is a major weapon in the Jewish-Zionist arsenal, that it is used to justify otherwise unjustifiable Israeli policies, and as a powerful tool for blackmailing enormous sums of money from Americans and Europeans.” \n", + "1052 “The modern trans movement is radicalizing activists into terrorists. […] Trans terrorism is on the rise. […] This sends a message: if you don’t let us mutilate children’s genitals, then we will shoot yours.” – GAG then-Secretary David Leatherwood, “Real America with Dan Ball,” in response to the Covenant School shooting in Nashville, Tennessee, \n", + "2485 “They [the Jews] control the media and so it spews lies against the enemy they fear most. And the Jews now misrule our beloved country of America, bringing it down by huge bribes, media brainwashing and blackmail of our officials, and police, and military officers both high and low. And worst of all, by a national network of organized pedophiles, child molesting and child-murdering adults who once involved in that network, one orgy at a time, are photographed there, captured forever on film. That film is stored away for future blackmail use. Those officials cannot quit even if they want to.” \n", + "203 “Look, I hate women. I think they deserve to be beaten, raped and locked in cages.” – Daily Stomer, July 2018 \n", + "1271 “If we don’t take a stand here, it’s only a matter of time before biological men will be able to share showers and locker rooms with women and girls all across America! … Many on the Left believe that biological men have a right to share showers, locker rooms, and bathrooms with women and little girls as long as they identify as female at that particular point in time — and that anyone who disagrees with that should be punished!” \n", + "297 “Immigration is a kind of proxy war—and maybe a last stand—for White Americans, who are undergoing a painful recognition that, unless dramatic action is taken, their grandchildren will live in a country that is alien and hostile.” \n", + "453 —“How Diversity Should be Nurtured,” \n", + "367 “Imagine how bad she [Hillary Clinton] smells, man? I’m told her and Obama, just stink, stink, stink, stink. You can’t wash that evil off, man. Told there’s a rotten smell around Hillary. I’m not kidding, people say, they say — folks, I’ve been told this by high-up folks. They say listen, Obama and Hillary both smell like sulfur. … I’ve talked to people that are in protective details, they’re scared of her. And they say listen, she’s a frickin’ demon and she stinks and so does Obama. I go, like what? Sulfur. They smell like hell.” — “The Alex Jones Show,” Oct. 10, 2016 \n", + "1055 “Every year, THOUSANDS of children are exposed to displays of sexual depravity at Pride events, and MILLIONS applaud it. This year, Gays Against Groomers will not stand for this destruction of childhood innocence. Be seeing you REAL soon.” – GAG Illinois X (formerly Twitter), \n", + "237 “I believe homosexuality is moral perversion and deserves no special consideration under the law. … I believe the South was right in the War Between the States, and I am not a racist.” \n", + "1553 —Nov. 16, 2013, videotaped rant against a resident while “patrolling” the streets of Leith \n", + "677 “The reality is, homosexuals have entered the Scouts in the past for predatory purposes.” " ] }, "metadata": {}, @@ -563,167 +549,162 @@ "output_type": "stream", "text": [ "\n", - "Number of unique extremists: 159\n", + "Number of unique extremists: 154\n", "Unique extremist quotes:\n", - "tucker-carlson 324\n", - "center-immigration-studies 286\n", - "matt-walsh 152\n", - "mike-cernovich 123\n", - "paul-nehlen 114\n", - "stefan-molyneux 28\n", - "greg-johnson 24\n", - "robert-spencer 24\n", - "gays-against-groomers 24\n", - "jack-posobiec 23\n", - "james-lindsay 23\n", - "veterans-patrol 22\n", - "roy-moore 22\n", - "alex-jones 21\n", - "faith-education-commerce 20\n", - "moms-liberty 19\n", - "act-america 19\n", - "august-kreis 19\n", - "family-research-council 19\n", - "pamela-geller 18\n", - "lieutenant-general-william-g-jerry-boykin-ret 17\n", - "proud-boys 17\n", - "david-yerushalmi 17\n", - "richard-bertrand-spencer 16\n", - "frazier-glenn-miller 15\n", - "malik-zulu-shabazz 15\n", - "pickup-artists-alpha-males-self-help 15\n", - "david-duke 15\n", - "roger-pearson 15\n", - "william-h-regnery-ii 15\n", - "center-security-policy 15\n", - "oath-keepers 14\n", - "joseph-francis-farah 14\n", - "michael-levin 14\n", - "cody-rutledge-wilson 14\n", - "elmer-stewart-rhodes 14\n", - "michael-flynn 14\n", - "david-lane 14\n", - "nation-islam 13\n", - "thomas-rousseau 13\n", - "matt-hale 12\n", - "louis-farrakhan 12\n", - "david-irving 12\n", - "scott-lively 12\n", - "peter-brimelow 12\n", - "base 12\n", - "john-tanton 12\n", - "andrew-anglin 12\n", - "bill-white 11\n", - "andrew-weev-auernheimer 11\n", - "charles-murray 11\n", - "nick-fuentes 11\n", - "male-supremacy 11\n", - "misogynist-incels 11\n", - "garrett-hardin 11\n", - "ernst-zundel 11\n", - "don-black 11\n", - "william-shockley 11\n", - "tom-metzger 10\n", - "matthew-heimbach 10\n", - "larry-klayman 10\n", - "vdare 10\n", - "ray-redfeairn 10\n", - "understanding-threat 10\n", - "james-mason 10\n", - "tomislav-sunic 10\n", - "linda-gottfredson 10\n", - "fred-phelps 10\n", - "michael-hill 10\n", - "raymond-cattell 10\n", - "paul-mullet 10\n", - "lydia-brimelow 10\n", - "asatru-folk-assembly 9\n", - "paul-elam 9\n", - "alt-right 9\n", - "michael-ralph-tubbs 9\n", - "jeff-berry 9\n", - "david-barton 9\n", - "craig-cobb 9\n", - "barnes-reviewfoundation-economic-liberty-inc 9\n", - "kyle-bristow 9\n", - "krisanne-hall 9\n", - "jean-philippe-rushton 9\n", - "louis-beam 9\n", - "edgar-steele 9\n", - "tim-wildmon 8\n", - "richard-butler 8\n", - "sam-francis 8\n", - "bo-gritz 8\n", - "tony-perkins 8\n", - "dan-stein 8\n", - "nathan-benjamin-damigo 8\n", - "daryush-roosh-valizadeh 8\n", - "bradley-dean-griffin 8\n", - "nationalist-social-club-nsc-131 8\n", - "remembrance-project 8\n", - "henry-harpending 8\n", - "kevin-strom 8\n", - "bryan-fischer 8\n", - "barry-black 8\n", - "paul-ray-ramsey 8\n", - "kevin-macdonald 8\n", - "frank-gaffney-jr 8\n", - "hal-turner 8\n", - "richard-lynn 7\n", - "jared-taylor 7\n", - "billy-roper 7\n", - "james-timothy-turner 7\n", - "johnny-monoxide-aka-john-ramondetta 7\n", - "atomwaffen-division 7\n", - "willis-carto 7\n", - "arthur-jensen 7\n", - "chuck-baldwin 7\n", - "aryan-brotherhood 7\n", - "wayne-lutton 6\n", - "american-freedom-party 6\n", - "alex-linder 6\n", - "chaya-raichik 6\n", - "thomas-robb 6\n", - "boyd-cathey 6\n", - "proenglish 6\n", - "gary-demar 6\n", - "gary-gerhard-lauck 6\n", - "ronald-doggett 6\n", - "radical-hebrew-israelites 6\n", - "michael-enoch-peinovich 6\n", - "stephen-miller 6\n", - "committee-open-debate-holocaust 5\n", - "michael-brian-vanderboegh-0 5\n", - "virginia-abernethy 5\n", - "april-gaede 5\n", - "larry-pratt 5\n", - "federation-american-immigration-reform 5\n", - "jeff-schoep 5\n", - "jamie-kelso 5\n", - "james-edwards 5\n", - "william-pierce 5\n", - "paul-cameron 5\n", - "kyle-rogers 5\n", - "james-wickstrom 5\n", - "glenn-spencer 4\n", - "barbara-coe 4\n", - "shaun-walker 4\n", - "william-daniel-johnson 4\n", - "ron-edwards 4\n", - "john-de-nugent 4\n", - "erich-gliebe 4\n", - "mark-weber 4\n", - "aryan-freedom-network 4\n", - "james-orien-allsup 4\n", - "kevin-lamb 4\n", - "paul-fromm 3\n", - "roan-garcia-quintana 3\n", - "cliff-kincaid 3\n", - "tom-deweese 3\n", - "jt-ready 3\n", - "vincent-bertollini 3\n", - "gordon-baum 3\n", - "harry-cooper 2\n", + "stefan-molyneux 28\n", + "robert-spencer 24\n", + "gays-against-groomers 24\n", + "greg-johnson 24\n", + "jack-posobiec 23\n", + "james-lindsay 23\n", + "veterans-patrol 22\n", + "roy-moore 22\n", + "alex-jones 21\n", + "faith-education-commerce 20\n", + "family-research-council 19\n", + "august-kreis 19\n", + "act-america 19\n", + "moms-liberty 19\n", + "pamela-geller 18\n", + "proud-boys 17\n", + "lieutenant-general-william-g-jerry-boykin-ret 17\n", + "david-yerushalmi 17\n", + "richard-bertrand-spencer 16\n", + "pickup-artists-alpha-males-self-help 15\n", + "david-duke 15\n", + "frazier-glenn-miller 15\n", + "center-security-policy 15\n", + "malik-zulu-shabazz 15\n", + "roger-pearson 15\n", + "william-h-regnery-ii 15\n", + "joseph-francis-farah 14\n", + "oath-keepers 14\n", + "elmer-stewart-rhodes 14\n", + "michael-levin 14\n", + "david-lane 14\n", + "cody-rutledge-wilson 14\n", + "michael-flynn 14\n", + "nation-islam 13\n", + "thomas-rousseau 13\n", + "matt-hale 12\n", + "david-irving 12\n", + "john-tanton 12\n", + "base 12\n", + "louis-farrakhan 12\n", + "peter-brimelow 12\n", + "andrew-anglin 12\n", + "scott-lively 12\n", + "bill-white 11\n", + "garrett-hardin 11\n", + "misogynist-incels 11\n", + "nick-fuentes 11\n", + "charles-murray 11\n", + "don-black 11\n", + "william-shockley 11\n", + "male-supremacy 11\n", + "ernst-zundel 11\n", + "andrew-weev-auernheimer 11\n", + "vdare 10\n", + "raymond-cattell 10\n", + "james-mason 10\n", + "linda-gottfredson 10\n", + "matthew-heimbach 10\n", + "understanding-threat 10\n", + "ray-redfeairn 10\n", + "tom-metzger 10\n", + "paul-mullet 10\n", + "lydia-brimelow 10\n", + "tomislav-sunic 10\n", + "larry-klayman 10\n", + "fred-phelps 10\n", + "michael-hill 10\n", + "louis-beam 9\n", + "paul-elam 9\n", + "asatru-folk-assembly 9\n", + "michael-ralph-tubbs 9\n", + "craig-cobb 9\n", + "barnes-reviewfoundation-economic-liberty-inc 9\n", + "edgar-steele 9\n", + "jean-philippe-rushton 9\n", + "alt-right 9\n", + "kyle-bristow 9\n", + "david-barton 9\n", + "jeff-berry 9\n", + "krisanne-hall 9\n", + "bo-gritz 8\n", + "nathan-benjamin-damigo 8\n", + "tim-wildmon 8\n", + "richard-butler 8\n", + "remembrance-project 8\n", + "barry-black 8\n", + "daryush-roosh-valizadeh 8\n", + "frank-gaffney-jr 8\n", + "bradley-dean-griffin 8\n", + "dan-stein 8\n", + "kevin-strom 8\n", + "bryan-fischer 8\n", + "kevin-macdonald 8\n", + "paul-ray-ramsey 8\n", + "henry-harpending 8\n", + "sam-francis 8\n", + "nationalist-social-club-nsc-131 8\n", + "tony-perkins 8\n", + "hal-turner 8\n", + "willis-carto 7\n", + "arthur-jensen 7\n", + "atomwaffen-division 7\n", + "billy-roper 7\n", + "james-timothy-turner 7\n", + "johnny-monoxide-aka-john-ramondetta 7\n", + "richard-lynn 7\n", + "jared-taylor 7\n", + "chuck-baldwin 7\n", + "aryan-brotherhood 7\n", + "american-freedom-party 6\n", + "chaya-raichik 6\n", + "thomas-robb 6\n", + "gary-gerhard-lauck 6\n", + "radical-hebrew-israelites 6\n", + "boyd-cathey 6\n", + "ronald-doggett 6\n", + "stephen-miller 6\n", + "michael-enoch-peinovich 6\n", + "gary-demar 6\n", + "alex-linder 6\n", + "proenglish 6\n", + "wayne-lutton 6\n", + "michael-brian-vanderboegh-0 5\n", + "jamie-kelso 5\n", + "kyle-rogers 5\n", + "virginia-abernethy 5\n", + "james-wickstrom 5\n", + "jeff-schoep 5\n", + "william-pierce 5\n", + "paul-cameron 5\n", + "larry-pratt 5\n", + "committee-open-debate-holocaust 5\n", + "federation-american-immigration-reform 5\n", + "james-edwards 5\n", + "april-gaede 5\n", + "john-de-nugent 4\n", + "erich-gliebe 4\n", + "glenn-spencer 4\n", + "shaun-walker 4\n", + "aryan-freedom-network 4\n", + "kevin-lamb 4\n", + "james-orien-allsup 4\n", + "william-daniel-johnson 4\n", + "barbara-coe 4\n", + "ron-edwards 4\n", + "mark-weber 4\n", + "roan-garcia-quintana 3\n", + "vincent-bertollini 3\n", + "tom-deweese 3\n", + "cliff-kincaid 3\n", + "gordon-baum 3\n", + "jt-ready 3\n", + "paul-fromm 3\n", + "harry-cooper 2\n", "Name: filename, dtype: int64\n" ] } @@ -753,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "81c31a4a", "metadata": {}, "outputs": [ @@ -762,7 +743,7 @@ "output_type": "stream", "text": [ "Max tokens in any paragraph: 322\n", - "Average tokens per paragraph: 42.7\n", + "Average tokens per paragraph: 49.7\n", "Number of paragraphs exceeding 512 tokens: 0\n", "\n", "Longest paragraph (322 tokens):\n", @@ -789,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "609a3723", "metadata": {}, "outputs": [ @@ -797,7 +778,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Segmented dataset shape: (2515, 4)\n" + "Segmented dataset shape: (1516, 4)\n" ] }, { @@ -829,37 +810,37 @@ " \n", " \n", " \n", - " 617\n", - " center-immigration-studies\n", - " washington times\n", + " 51\n", + " richard-bertrand-spencer\n", + " “ we ’ re going to be back here, and we ’ re going to humiliate all of these people who opposed us. we ’ ll be back here 1, 000 times if necessary. i always win. because i have the will to win, i keep going until i win. ”\n", " 0\n", " 1\n", " \n", " \n", - " 927\n", - " louis-beam\n", - " a “ new world order, ” 1999 essay by beam\n", + " 168\n", + " tim-wildmon\n", + " a [ islam ] is in fact a religion of war, violence, intolerance, and physical persecution of non - muslims. a\n", " 0\n", " 1\n", " \n", " \n", - " 942\n", - " bill-white\n", - " in august 2004, white was convicted of assaulting a woman when she was handing out flyers that identified him as a neo - nazi landlord. during the court proceedings, white cursed at the woman from the witness stand and was held in contempt of court by the judge. in december 2009, a federal jury found white guilty of threatening several people through intimidating phone calls and internet postings. he was sentenced to 2 a½ years in prison at an april 2010 hearing. in january 2011, white was f...\n", + " 1469\n", + " misogynist-incels\n", + " “ jews heavily pushed feminism. roasties have such high standards because they are ‘ liberated ’. you probably would have gotten your d * * * wet by now if it weren ’ t for feminism and for jews. ”\n", " 0\n", " 1\n", " \n", " \n", - " 973\n", - " william-pierce\n", - " a pierce, quoted in a biography of him called fame of a dead man ’ s deeds, on the power of narrative\n", + " 926\n", + " kyle-bristow\n", + " a quoted in the\n", " 0\n", " 1\n", " \n", " \n", - " 1967\n", - " tucker-carlson\n", - " a reporter later exposed\n", + " 422\n", + " raymond-cattell\n", + " asuppose, as may well be the case, that one of these races is naturally courageous, self - sacrificing and enterprising and the other less so. the group will continue to prosper owing to the activities of inventors and explorers of the first race, who, as is generally the rule, will not pass on the usual number of children to the next generation. the nation will be successful in war because the same race has actively responded to the call to arms and to self - sacrifice. throughout these act...\n", " 0\n", " 1\n", " \n", @@ -871,37 +852,37 @@ " ...\n", " \n", " \n", - " 866\n", - " barry-black\n", - " a a 1998 comment to a virginia newspaper\n", + " 1164\n", + " craig-cobb\n", + " anov. 16, 2013, videotaped rant against a resident while apatrollinga the streets of leith\n", " 0\n", " 1\n", " \n", " \n", - " 471\n", - " david-yerushalmi\n", - " a aoffensive and defensive lawfare : fighting civilization jihad in americaas courts, a center for security policy press release,\n", + " 677\n", + " family-research-council\n", + " “ the reality is, homosexuals have entered the scouts in the past for predatory purposes. ”\n", " 0\n", " 1\n", " \n", " \n", - " 1174\n", - " kevin-strom\n", - " “ the aryan race, by dint of its intelligence and creativity and character has managed to drag itself up to a state of civilization and some degree of scientific understanding of the universe about us. but what dr. pierce could clearly see, and what the more jingoistic racialists cannot see, is that that state of civilization is but a few inches above the slime of universal savagery. a¦ the journey has just begun and the danger of falling back is very great. ”\n", + " 123\n", + " base\n", + " “ most of our members are national socialists and / or fascists, although we also have some run - of - the - mill white nationalists … we have a strong revolutionary and militant current running through the base. most of our members are pretty hardcore in that sense. you ’ re going to be stepping into probably the most extreme group of pro - white people that you can probably come across. ” –\n", " 0\n", " 1\n", " \n", " \n", - " 56\n", - " david-barton\n", - " “ and we don ’ t want racism in america. but then as you started watching, that ’ s not what this was about. this was about a hate america movement. ” in reference to cancel culture, the 1619 project, and covid - era anti - racism protests. – the elephant heard podcast, april 27, 2021\n", + " 706\n", + " scott-lively\n", + " “ the gay movement is an evil institution [ whose ] goal is to defeat the marriage - based society and replace it with a culture of sexual promiscuity in which thereas no restrictions on sexual conduct except the principle of mutual choice. ”\n", " 0\n", " 1\n", " \n", " \n", - " 1457\n", - " barnes-reviewfoundation-economic-liberty-inc\n", - " ajewish involvement in the communist revolution by john wear, j. d. radical jewish activists, political instigators, and criminals were the main protagonists of the arussiana revolution, which overthrew the tzar, resulting in the brutal murder of the royal family and millions of gentiles. john wear highlights and documents the overwhelming evidence that jews played the leading role in this tragedy, using a variety of sources. a\n", + " 602\n", + " jean-philippe-rushton\n", + " “ whites have, on average, more neurons and cranial size than blacks … blacks have an advantage in sport because they have narrower hips — but they have narrower hips because they have smaller brains. ”\n", " 0\n", " 1\n", " \n", @@ -911,44 +892,44 @@ "" ], "text/plain": [ - " filename \\\n", - "617 center-immigration-studies \n", - "927 louis-beam \n", - "942 bill-white \n", - "973 william-pierce \n", - "1967 tucker-carlson \n", - "... ... \n", - "866 barry-black \n", - "471 david-yerushalmi \n", - "1174 kevin-strom \n", - "56 david-barton \n", - "1457 barnes-reviewfoundation-economic-liberty-inc \n", + " filename \\\n", + "51 richard-bertrand-spencer \n", + "168 tim-wildmon \n", + "1469 misogynist-incels \n", + "926 kyle-bristow \n", + "422 raymond-cattell \n", + "... ... \n", + "1164 craig-cobb \n", + "677 family-research-council \n", + "123 base \n", + "706 scott-lively \n", + "602 jean-philippe-rushton \n", "\n", " paragraph_segment \\\n", - "617 washington times \n", - "927 a “ new world order, ” 1999 essay by beam \n", - "942 in august 2004, white was convicted of assaulting a woman when she was handing out flyers that identified him as a neo - nazi landlord. during the court proceedings, white cursed at the woman from the witness stand and was held in contempt of court by the judge. in december 2009, a federal jury found white guilty of threatening several people through intimidating phone calls and internet postings. he was sentenced to 2 a½ years in prison at an april 2010 hearing. in january 2011, white was f... \n", - "973 a pierce, quoted in a biography of him called fame of a dead man ’ s deeds, on the power of narrative \n", - "1967 a reporter later exposed \n", + "51 “ we ’ re going to be back here, and we ’ re going to humiliate all of these people who opposed us. we ’ ll be back here 1, 000 times if necessary. i always win. because i have the will to win, i keep going until i win. ” \n", + "168 a [ islam ] is in fact a religion of war, violence, intolerance, and physical persecution of non - muslims. a \n", + "1469 “ jews heavily pushed feminism. roasties have such high standards because they are ‘ liberated ’. you probably would have gotten your d * * * wet by now if it weren ’ t for feminism and for jews. ” \n", + "926 a quoted in the \n", + "422 asuppose, as may well be the case, that one of these races is naturally courageous, self - sacrificing and enterprising and the other less so. the group will continue to prosper owing to the activities of inventors and explorers of the first race, who, as is generally the rule, will not pass on the usual number of children to the next generation. the nation will be successful in war because the same race has actively responded to the call to arms and to self - sacrifice. throughout these act... \n", "... ... \n", - "866 a a 1998 comment to a virginia newspaper \n", - "471 a aoffensive and defensive lawfare : fighting civilization jihad in americaas courts, a center for security policy press release, \n", - "1174 “ the aryan race, by dint of its intelligence and creativity and character has managed to drag itself up to a state of civilization and some degree of scientific understanding of the universe about us. but what dr. pierce could clearly see, and what the more jingoistic racialists cannot see, is that that state of civilization is but a few inches above the slime of universal savagery. a¦ the journey has just begun and the danger of falling back is very great. ” \n", - "56 “ and we don ’ t want racism in america. but then as you started watching, that ’ s not what this was about. this was about a hate america movement. ” in reference to cancel culture, the 1619 project, and covid - era anti - racism protests. – the elephant heard podcast, april 27, 2021 \n", - "1457 ajewish involvement in the communist revolution by john wear, j. d. radical jewish activists, political instigators, and criminals were the main protagonists of the arussiana revolution, which overthrew the tzar, resulting in the brutal murder of the royal family and millions of gentiles. john wear highlights and documents the overwhelming evidence that jews played the leading role in this tragedy, using a variety of sources. a \n", + "1164 anov. 16, 2013, videotaped rant against a resident while apatrollinga the streets of leith \n", + "677 “ the reality is, homosexuals have entered the scouts in the past for predatory purposes. ” \n", + "123 “ most of our members are national socialists and / or fascists, although we also have some run - of - the - mill white nationalists … we have a strong revolutionary and militant current running through the base. most of our members are pretty hardcore in that sense. you ’ re going to be stepping into probably the most extreme group of pro - white people that you can probably come across. ” – \n", + "706 “ the gay movement is an evil institution [ whose ] goal is to defeat the marriage - based society and replace it with a culture of sexual promiscuity in which thereas no restrictions on sexual conduct except the principle of mutual choice. ” \n", + "602 “ whites have, on average, more neurons and cranial size than blacks … blacks have an advantage in sport because they have narrower hips — but they have narrower hips because they have smaller brains. ” \n", "\n", " segment_id label \n", - "617 0 1 \n", - "927 0 1 \n", - "942 0 1 \n", - "973 0 1 \n", - "1967 0 1 \n", + "51 0 1 \n", + "168 0 1 \n", + "1469 0 1 \n", + "926 0 1 \n", + "422 0 1 \n", "... ... ... \n", - "866 0 1 \n", - "471 0 1 \n", - "1174 0 1 \n", - "56 0 1 \n", - "1457 0 1 \n", + "1164 0 1 \n", + "677 0 1 \n", + "123 0 1 \n", + "706 0 1 \n", + "602 0 1 \n", "\n", "[33 rows x 4 columns]" ] @@ -986,18 +967,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "d5092eff-7e7d-4129-8e01-bbf9b28ee016", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + "Running with: ~/workspace/Sentinel/.venv/bin/python\n" ] }, { @@ -1005,23 +983,11 @@ "output_type": "stream", "text": [ "Loading neutral podcast dataset directly from Hugging Face...\n", - "Using cached file at /Users/evonck/.cache/huggingface/neutral/lex-fridman-podcastUsing cached file.parquet\n", + "Using cached file at ~/.cache/huggingface/neutral/lex-fridman-podcastUsing cached file.parquet\n", "Successfully loaded data with 346 rows\n", - "Processed 346 episodes with segments\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Extracting segments: 100%|██████████| 316/316 [00:00<00:00, 3474.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collected 750250 segments before sampling\n", + "Processed 346 episodes with segments\n", + "Extracting segments: 100%|█████████████████| 316/316 [00:00<00:00, 3296.62it/s]\n", + "Collected 761946 segments before sampling\n", "Randomly sampled 15000 segments\n", "Saved 15000 training segments to neutral-segments-training.csv\n", "Saved 30 evaluation episodes to neutral-episodes-eval.parquet\n" @@ -1029,26 +995,48 @@ } ], "source": [ - "%%bash\n", - "# Change directory into the 'scripts' folder\n", - "cd scripts\n", - "\n", - "# Run your Python script using poetry run\n", - "# This ensures all your Poetry-managed dependencies are available\n", - "poetry run python fetch-neutral-examples-data.py\n", - "\n", - "# The script will create two files:\n", - "# 1. `neutral-segments-training.csv`: Contains ~15,000 individual segments (10x our positive examples) for training\n", - "# 2. `neutral-episodes-eval.parquet`: Contains 30 full episodes for evaluation\n", - "# You can customize the number of segments and evaluation episodes:\n", - "# ```bash\n", - "# python fetch-neutral-examples-data.py -n 20000 -e 50 # 20k segments, 50 eval episodes\n", - "# ```" + "# ============================================================================\n", + "# ORIGINAL CODE (kept for reference, commented out).\n", + "# It failed with \"poetry: command not found\" because a %%bash cell starts a\n", + "# plain non-login shell that never reads ~/.zshrc, so ~/.local/bin (where\n", + "# poetry is installed) is missing from PATH.\n", + "# ============================================================================\n", + "# %%bash\n", + "# # Change directory into the 'scripts' folder\n", + "# cd scripts\n", + "#\n", + "# # Run your Python script using poetry run\n", + "# # This ensures all your Poetry-managed dependencies are available\n", + "# poetry run python fetch-neutral-examples-data.py\n", + "#\n", + "# # The script will create two files:\n", + "# # 1. `neutral-segments-training.csv`: Contains ~15,000 individual segments (10x our positive examples) for training\n", + "# # 2. `neutral-episodes-eval.parquet`: Contains 30 full episodes for evaluation\n", + "# # You can customize the number of segments and evaluation episodes:\n", + "# # ```bash\n", + "# # python fetch-neutral-examples-data.py -n 20000 -e 50 # 20k segments, 50 eval episodes\n", + "# # ```\n", + "\n", + "# ============================================================================\n", + "# WORKING VERSION\n", + "# ============================================================================\n", + "import sys\n", + "\n", + "# This notebook's kernel already runs inside the Poetry virtualenv\n", + "# (Sentinel/.venv), so sys.executable has every dependency the script needs and\n", + "# the `poetry run` wrapper is unnecessary.\n", + "print(\"Running with:\", sys.executable)\n", + "\n", + "# The script writes two files into examples/data/ :\n", + "# 1. neutral-segments-training.csv -> ~15,000 individual segments for training\n", + "# 2. neutral-episodes-eval.parquet -> 30 full episodes for evaluation\n", + "# To change those amounts, append flags, e.g. `... -n 20000 -e 50`\n", + "!cd scripts && {sys.executable} fetch-neutral-examples-data.py" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "922bed03", "metadata": {}, "outputs": [ @@ -1056,36 +1044,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of positive examples: 2515\n", - "Number of negative examples to extract: 25150\n", + "Number of positive examples: 1516\n", + "Number of negative examples to extract: 15160\n", "Loading neutral podcast dataset directly from Hugging Face...\n", - "Using cached file at /Users/evonck/.cache/huggingface/lex-fridman-podcast/neutral-episodes-eval.parquet\n", + "Using cached file at ~/.cache/huggingface/lex-fridman-podcast/neutral-episodes-eval.parquet\n", "Successfully loaded data with 346 rows\n", "Processed 346 episodes with segments\n", "Dataset loaded with 346 episodes\n", "Extracting random segments...\n" ] }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4968b5e5b3804c72b40b1ab265589e5c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Extracting segments: 0%| | 0/346 [00:00\n", " 0\n", " neutral_podcast\n", - " And I feel like the imagination is a really powerful tool\n", + " and gave people a big massive lesson in civics.\n", " 0\n", " 0\n", " \n", " \n", " 1\n", " neutral_podcast\n", - " Oh, that was like the smaller one, like the firefly.\n", + " I'm humiliated about that kind of stuff\n", " 1\n", " 0\n", " \n", " \n", " 2\n", " neutral_podcast\n", - " Computers weren't really great at that.\n", + " I don't know.\n", " 2\n", " 0\n", " \n", " \n", " 3\n", " neutral_podcast\n", - " to sort of pretend that there's something that they're not in order to understand what's\n", + " Yeah, like it made me imagine\n", " 3\n", " 0\n", " \n", " \n", " 4\n", " neutral_podcast\n", - " to math dot square root.\n", + " but people love it when I ask it,\n", " 4\n", " 0\n", " \n", @@ -1158,19 +1132,12 @@ "" ], "text/plain": [ - " filename \\\n", - "0 neutral_podcast \n", - "1 neutral_podcast \n", - "2 neutral_podcast \n", - "3 neutral_podcast \n", - "4 neutral_podcast \n", - "\n", - " paragraph_segment \\\n", - "0 And I feel like the imagination is a really powerful tool \n", - "1 Oh, that was like the smaller one, like the firefly. \n", - "2 Computers weren't really great at that. \n", - "3 to sort of pretend that there's something that they're not in order to understand what's \n", - "4 to math dot square root. \n", + " filename paragraph_segment \\\n", + "0 neutral_podcast and gave people a big massive lesson in civics. \n", + "1 neutral_podcast I'm humiliated about that kind of stuff \n", + "2 neutral_podcast I don't know. \n", + "3 neutral_podcast Yeah, like it made me imagine \n", + "4 neutral_podcast but people love it when I ask it, \n", "\n", " segment_id label \n", "0 0 0 \n", @@ -1188,7 +1155,7 @@ "output_type": "stream", "text": [ "\n", - "Combined dataset shape: (27665, 4)\n", + "Combined dataset shape: (16676, 4)\n", "Sample of combined dataset:\n" ] }, @@ -1221,38 +1188,38 @@ " \n", " \n", " \n", - " 22239\n", + " 4990\n", " neutral_podcast\n", - " It wasn't because we were evil rhino haters as a whole.\n", - " 19724\n", + " Really?\n", + " 3474\n", " 0\n", " \n", " \n", - " 11674\n", + " 3781\n", " neutral_podcast\n", - " Yeah, it also probably says that it's quite useful\n", - " 9159\n", + " If land wars turn into an inescapable quagmire each time\n", + " 2265\n", " 0\n", " \n", " \n", - " 4097\n", + " 11523\n", " neutral_podcast\n", - " That's the problem.\n", - " 1582\n", + " And for the military, in some sense,\n", + " 10007\n", " 0\n", " \n", " \n", - " 5219\n", - " neutral_podcast\n", - " because anybody can always come back\n", - " 2704\n", + " 47\n", + " hal-turner\n", + " on june 3, 2009, turner was arrested and charged with inciting injury to persons or property. in a blog post, he had asked connecticut catholics to “ take up arms ” against two lawmakers and an employee of the office of state ethics. turner also threatened to release the home addresses of these three men. a jury acquitted turner of the charges in september 2011.\n", " 0\n", + " 1\n", " \n", " \n", - " 3601\n", + " 13262\n", " neutral_podcast\n", - " to try to unlock over a five to 10 year period\n", - " 1086\n", + " through that decision tree that Ryan was presenting,\n", + " 11746\n", " 0\n", " \n", " \n", @@ -1263,38 +1230,38 @@ " ...\n", " \n", " \n", - " 15548\n", - " neutral_podcast\n", - " And they'll do just fine in those areas as long as pedestrians don't mess with them too\n", - " 13033\n", + " 99\n", + " roy-moore\n", + " “ we were torn apart in the civil war — brother against brother, north against south, party against party.. what changed? now we have blacks and whites fighting, reds and yellows fighting, democrats and republicans fighting, men and women fighting. ” — speaking at a rally in florence, alabama, september,\n", " 0\n", + " 1\n", " \n", " \n", - " 17636\n", + " 2857\n", " neutral_podcast\n", - " So, so the original one was CASP or critical assessment of of protein structure.\n", - " 15121\n", + " Right.\n", + " 1341\n", " 0\n", " \n", " \n", - " 8246\n", + " 3115\n", " neutral_podcast\n", - " As William James said, death is the warm at the core of the human condition.\n", - " 5731\n", + " Yeah, so if you dig in on the idea of this reference frame,\n", + " 1599\n", " 0\n", " \n", " \n", - " 1972\n", - " tucker-carlson\n", - " jonah bennett, who\n", + " 4972\n", + " neutral_podcast\n", + " an important moment in human history.\n", + " 3456\n", " 0\n", - " 1\n", " \n", " \n", - " 13943\n", + " 6335\n", " neutral_podcast\n", - " you can learn what it is to wrestle with difficult ideas\n", - " 11428\n", + " I guess I shit the bed because he looks at me and he just goes, okay. All right. Okay. Well,\n", + " 4819\n", " 0\n", " \n", " \n", @@ -1304,43 +1271,43 @@ ], "text/plain": [ " filename \\\n", - "22239 neutral_podcast \n", - "11674 neutral_podcast \n", - "4097 neutral_podcast \n", - "5219 neutral_podcast \n", - "3601 neutral_podcast \n", + "4990 neutral_podcast \n", + "3781 neutral_podcast \n", + "11523 neutral_podcast \n", + "47 hal-turner \n", + "13262 neutral_podcast \n", "... ... \n", - "15548 neutral_podcast \n", - "17636 neutral_podcast \n", - "8246 neutral_podcast \n", - "1972 tucker-carlson \n", - "13943 neutral_podcast \n", + "99 roy-moore \n", + "2857 neutral_podcast \n", + "3115 neutral_podcast \n", + "4972 neutral_podcast \n", + "6335 neutral_podcast \n", "\n", - " paragraph_segment \\\n", - "22239 It wasn't because we were evil rhino haters as a whole. \n", - "11674 Yeah, it also probably says that it's quite useful \n", - "4097 That's the problem. \n", - "5219 because anybody can always come back \n", - "3601 to try to unlock over a five to 10 year period \n", - "... ... \n", - "15548 And they'll do just fine in those areas as long as pedestrians don't mess with them too \n", - "17636 So, so the original one was CASP or critical assessment of of protein structure. \n", - "8246 As William James said, death is the warm at the core of the human condition. \n", - "1972 jonah bennett, who \n", - "13943 you can learn what it is to wrestle with difficult ideas \n", + " paragraph_segment \\\n", + "4990 Really? \n", + "3781 If land wars turn into an inescapable quagmire each time \n", + "11523 And for the military, in some sense, \n", + "47 on june 3, 2009, turner was arrested and charged with inciting injury to persons or property. in a blog post, he had asked connecticut catholics to “ take up arms ” against two lawmakers and an employee of the office of state ethics. turner also threatened to release the home addresses of these three men. a jury acquitted turner of the charges in september 2011. \n", + "13262 through that decision tree that Ryan was presenting, \n", + "... ... \n", + "99 “ we were torn apart in the civil war — brother against brother, north against south, party against party.. what changed? now we have blacks and whites fighting, reds and yellows fighting, democrats and republicans fighting, men and women fighting. ” — speaking at a rally in florence, alabama, september, \n", + "2857 Right. \n", + "3115 Yeah, so if you dig in on the idea of this reference frame, \n", + "4972 an important moment in human history. \n", + "6335 I guess I shit the bed because he looks at me and he just goes, okay. All right. Okay. Well, \n", "\n", " segment_id label \n", - "22239 19724 0 \n", - "11674 9159 0 \n", - "4097 1582 0 \n", - "5219 2704 0 \n", - "3601 1086 0 \n", + "4990 3474 0 \n", + "3781 2265 0 \n", + "11523 10007 0 \n", + "47 0 1 \n", + "13262 11746 0 \n", "... ... ... \n", - "15548 13033 0 \n", - "17636 15121 0 \n", - "8246 5731 0 \n", - "1972 0 1 \n", - "13943 11428 0 \n", + "99 0 1 \n", + "2857 1341 0 \n", + "3115 1599 0 \n", + "4972 3456 0 \n", + "6335 4819 0 \n", "\n", "[50 rows x 4 columns]" ] @@ -1352,6 +1319,10 @@ "source": [ "# Calculate how many negative examples we need (10x the number of positives)\n", "\n", + "# Seeded so the negative segments sampled below are the same on every run and\n", + "# the numbers reported later in the notebook stay comparable.\n", + "random.seed(42)\n", + "\n", "num_positives = len(segmented_df)\n", "num_negatives = num_positives * 10\n", "print(f\"Number of positive examples: {num_positives}\")\n", @@ -1473,7 +1444,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "e966e3b9", "metadata": {}, "outputs": [ @@ -1482,8 +1453,8 @@ "output_type": "stream", "text": [ "Extracting positive and negative examples...\n", - "Number of positive examples: 2515\n", - "Number of negative examples: 25150\n", + "Number of positive examples: 1516\n", + "Number of negative examples: 15160\n", "\n", "Encoding positive examples...\n", "\n", @@ -1552,7 +1523,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "bd2f023f-b51f-4f66-b0ab-296439378c03", "metadata": {}, "outputs": [ @@ -1562,16 +1533,6 @@ "text": [ "The directory 'data/podcast_examples' already exists. Skipping git clone.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] } ], "source": [ @@ -1594,7 +1555,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "a374928d-5c31-4f7e-951f-58f171ad90a7", "metadata": {}, "outputs": [ @@ -1602,7 +1563,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 9682 transcript files.\n" + "Found 10396 transcript files.\n" ] } ], @@ -1610,7 +1571,9 @@ "# List transcript files and load a sample for evaluation\n", "data_dir = \"data/podcast_examples\" # Define data_dir for this cell\n", "\n", - "transcript_files = glob.glob(os.path.join(data_dir, \"transcripts\", \"**\", \"*.txt\"), recursive=True)\n", + "# sorted() because glob returns filesystem order, which differs between machines.\n", + "# Without it the \"first 100\" episodes used below are not the same set twice.\n", + "transcript_files = sorted(glob.glob(os.path.join(data_dir, \"transcripts\", \"**\", \"*.txt\"), recursive=True))\n", "print(f\"Found {len(transcript_files)} transcript files.\")\n", "\n", "# Check if transcript files were found before trying to open one\n", @@ -1626,7 +1589,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "455888f7", "metadata": {}, "outputs": [ @@ -1634,23 +1597,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loading saved Sentinel index...\n", - "Index loaded successfully!\n" + "Loading saved Sentinel index...\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0bc758d716b0447a9afba4dd7cd4610c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing transcripts: 0%| | 0/100 [00:00 512). Running this sequence through the model will result in indexing errors\n" + "Token indices sequence length is longer than the specified maximum sequence length for this model (77886 > 512). Running this sequence through the model will result in indexing errors\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Created 2731 segments\n" + "Created 52185 segments\n" ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "82f14a11b74146348239229229bd63dd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Calculating rare class affinity: 0%| | 0/100 [00:00\\s*\\d{2}:\\d{2}:\\d{2}\\s*', '', cleaned)\n", " return cleaned.strip()\n", "\n", + "# Take a random sample of 100 episodes rather than the first 100. The corpus\n", + "# is grouped by show, so an alphabetical slice would cover only the shows that\n", + "# sort first and would miss the rest entirely. Seeded so the same 100 episodes\n", + "# are drawn on every run.\n", + "random.seed(42)\n", + "selected_transcripts = random.sample(transcript_files, min(100, len(transcript_files)))\n", + "\n", "# Load and clean transcripts\n", "cleaned_transcripts = []\n", - "for file_path in tqdm(transcript_files[:100], desc=\"Processing transcripts\"): # Sample 100 episodes\n", + "for file_path in tqdm(selected_transcripts, desc=\"Processing transcripts\"):\n", " try:\n", " with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:\n", " text = f.read()\n", @@ -1784,7 +1718,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "8e1c158c", "metadata": {}, "outputs": [ @@ -1796,20 +1730,6 @@ "Selected 30 random Lex Fridman podcast episodes\n" ] }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "827df5b146bf496bb89d8188ed756c04", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing Neutral episodes: 0%| | 0/30 [00:00" ] @@ -1971,7 +1857,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1987,18 +1873,18 @@ "Statistics for hate affinity scores:\n", "\n", "Controversial:\n", - " Mean: 0.0796\n", - " Median: 0.0000\n", - " Std Dev: 0.1947\n", - " Min: -0.5694\n", - " Max: 0.7799\n", + " Mean: 0.1185\n", + " Median: 0.1118\n", + " Std Dev: 0.0722\n", + " Min: 0.0000\n", + " Max: 0.3922\n", "\n", "Lex Fridman:\n", - " Mean: 0.0639\n", - " Median: 0.0594\n", - " Std Dev: 0.0754\n", + " Mean: 0.0355\n", + " Median: 0.0000\n", + " Std Dev: 0.0967\n", " Min: 0.0000\n", - " Max: 0.3032\n" + " Max: 0.5095\n" ] } ], @@ -2077,7 +1963,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "55dbe289", "metadata": {}, "outputs": [ @@ -2094,8 +1980,8 @@ "- Lex Fridman: 0 segments (0.00% of all segments)\n", "\n", "Medium risk segments (0.1 < score <= 0.5):\n", - "- Controversial: 236 segments (8.64% of all segments)\n", - "- Lex Fridman: 67 segments (0.94% of all segments)\n" + "- Controversial: 976 segments (1.87% of all segments)\n", + "- Lex Fridman: 72 segments (1.03% of all segments)\n" ] } ], @@ -2127,7 +2013,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "74e25bf5", "metadata": {}, "outputs": [ @@ -2138,17 +2024,9 @@ "Saved all segment-level data to CSV files\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/ph/f73k11ns3_v8n6g3lrdb792w0000gr/T/ipykernel_89229/3057986402.py:33: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", - " box = plt.boxplot(segment_data, patch_artist=True, labels=platforms)\n" - ] - }, { "data": { - "image/png": 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", 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", 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Ngc6Igk6Prr/mSGtEiHpY9R6oB1nJGEOP17BhQ7cMZkbSNpTkBfXIK++AepSVp0IjNFQuzYPPymge7aPPo84zo/clO7kS1CiowF83XUuN4FDve1aWngwtT1brU15c/0h9L2hUjnrvlf9BvfPKfaFgP6+k/Z72rqPm7WcUtIfmBVEOBI2k0AgIjbBQ/oZhw4a5fAZNmjTJszID8CeCfQCIAP2IEw1tPRr1aKlHXzf9KH7iiSdc77EaANR7eLREUjnh9SiF/khW0qvQgE89ZRktJ6fesNBhq9kpm35cK0u8pjWE9u4r67X3eCToOOod1o/p0N79SL+O16uq66ZrqiG46sHUualRJKOe37Tl/P777931D72OoVnvIyE7ZcqOzN57DU8OdbSkaWlppINGw2SlnEpOp4YFNY4poZkCn7TZ0nU89WTrsxXpz1FWqLdZvcfq2dcQ9cw+g5JZ+XQOqiPqGQ5t7Mgtb7lQjVIJlVHZlExPGfK9Hvqs1qesXn/tp8+rPg9qEMlP+q7T6yuBoXjfb979jOg66HqsWbMm3WP6ntH3zrEaMrwpShr5lZX6rv3Vu6+b3iNdp5EjR4at9gIAWcGcfQDIJQUeQ4YMcT/QlcU8Mxrim5b3Y9ebQ+qtxxyptdyVjTo0j4CWb9MPfmX0D/1hqUzhoWtPa535tEt9Zadsl156qQsQNGQ2lIYTKxAIff3c0OtoKHnofO7Dhw+77OXqBVW29+zSj2tlvk5L56350AoYFACo11G9/ZrTnFGwoOG/HjUCKet86Lxpzel++eWXLZKyU6bs0Huf0TB0BS6ht7Q9/cfqBdf1VO9lRtda76NHAZWGoStDuhrW9FjoEH7veLrGGV1TrdrgZUXPK14vdNpeZ60qkFZmnyVN69Fx1GCQ9ji6H7qEX0bUaJhRr7eXoyPtUHRd/9BcAvrMK6eIMuKrHNmpT1m9/hqZofdTWfjTjnaI1EgeNTpktIKCGjDVyOBdB32O1ZCkLP9pP/NeWXQNdD10XUKnP2mVAk0tUeNT2qX80tLnX/uocVfTTzK7jpouoO+FUPp+VoNLfi0fCcBf6NkHgGzQ8Fr15ijY0I89Bfrq3VTPrQI5JV3LjH7cahi/1lbW/prDqqWuNCdYPxi9H3ZKxDZu3Dj3A09BgRI4pZ0DmlVao1vH1hBelVeBh6YahCaGUw4BNQJoqLF+sGtOrXqQQhPmZbdsGobasmVL1/uqH8gabq2pCvrBrGGqaY+dU1oqS8O7NXR82bJlbmkqnYuWM9S5ps0ZkNVAQYm31CCh5GC6hgpiJk+ebBs3bnTH9QI7LVunAEvXQddU85DVqKMASiMbvAYeLS2nhg8l6NLSewqKNZTYqy+R7InOapmyQ0nc1KDSp08fNyxcDSl6j49FQaI3yiKUhoRrqLs+M5qT7C2npoBQy7XpPVS98ZalEwX3asTRMHQNF/fyMoQuf6nh/VqWUOevZRPV4KTX13Y1Kng93HlBwZwCR80RV0Cn/Bqq8+vXr0+3r5cUT58PTVHQqBFdT30uNLxcyw/q/BUYqw7rGJp7r/quJRIzo+RzChg1XF3THNSAp2kFeu/02Uib3FNz1RWIhi69J2psyG59yur11/ePzlsNpPp8qYFDr71kyRKX50ND1nNL38mqJ0pUqHnvqq+an6+gXkGzlv3zaJlPfUc2bdrUXV99n+naa6qVchyI3hMdU/tpWT0Nu9f3jo6l9zsrdUPL7Oka6XX0nquhQQ0Meh1dK30/aFSFRkboe1jXWa+j913f3aEJOwEgyyKS0x8AfM5bUsm7aRmmypUrBy6++GK3VFnoEm+ZLb03Z86cQMeOHQNVq1Z1z9dfLeuWdumxDz/8MFC/fn231Fbosmpakq1BgwYZli+zpfe07Fe/fv0CSUlJgWLFirkl1DJaQk5Lw2mZPi1rde6557rluNIe82hlS7v0nrfcVO/evd15FipUKFC7dm23pFToklai4/Ts2TNdmbK6PJqW07r55psDFSpUcNe1YcOGGS4PmNWl93S8J5980p27lhzTuZYrVy7QqlWrwDvvvJPh/ip/9erV3XmqXrRu3Trw0ksvhe23bt069/p6HypWrOiWD3v33Xfd+Ycup5fZ+5xZ+TO6flkpU2bL42k5uLTL+e3evTtw3XXXueXDvOXMjsY7dmY3b3k21RHVz1q1arn3Tu/hP/7xj8DTTz+dbrk41RudT0ZLOnr0nKeeespdP9VlvW/NmjULDBo0KLBz584cLb2XUd3MbDnOP/74wy0pp+ukJdWuuuoqt0Sb9tP3QdqlB/WZ0zJ0aZfhU73Q8m1amlA3LdGocqxZs+ao5f30008Dt9xyi9tfS0/qmura3nvvvemWnfPOTctW6rOp66WlQ0OXgcxuHc/q9ZcJEya41/P2U72fPXv2Met7Rt9LaemzNmDAgMA555zjvvv0GdZnTsf7/PPP0+2/atWq4PumpQe1bN+jjz4ats8333wTaNeunbuuxYsXD7Rs2TKwYMGCbC3RqmurY6hu6HVOPfVUtyygt/yhljfUddb7p/dd+zVv3twtmQoAOZGg/2S9aQAAAESKRgn07t3bJXhUTzCQXzSaREtKpp1qAwDwD+bsAwCQDzRvOZTm5moosJY/I9AHAACRxpx9AADygeYma91wJWVUsjvlRdB8Zs3dBwAAiDSCfQAA8oESoY0fP94F90pcpgRcb775Zrqs8gAAAJHAnH0AAAAAAHyGOfsAAAAAAPgMwT4AAAAAAD7DnP0sSE1NtY0bN1qpUqXcUjUAAAAAAOQlzbjftWuXVa1a1RITs99PT7CfBQr0q1evHu1iAAAAAACOM7///rtVq1Yt288j2M8C9eh7F7l06dIW66MQtm7dahUrVsxR6w8QLdRdxDPqL+IVdRfxjPoLv9fdlJQU1+nsxaPZRbCfBd7QfQX68RDs79+/35WTLz3EE+ou4hn1F/GKuot4Rv3F8VJ3E3I4lZxPBQAAAAAAPkOwDwAAAACAzxDsAwAAAADgM8zZBwAAAIAILpd2+PBhO3LkSLSLghies3/o0CE3b79IkSJWoECBPHkdgn0AAAAAiICDBw/apk2bbO/evdEuCmK8QUgB/65du1yCPi2rV7JkyYi/DsE+AAAAAOSSgrf169e7XtqqVata4cKFc5xFHcfH6I8CBQrYX3/9ZX/88YfVrl074j38BPsAAAAAEIFefQX8Whe9ePHi0S4O4iDYL1iwoFWsWNF+/fVXN6w/0sE+CfoAAAAAIEKysm464MnL0R/URAAAAAAAfIZgHwAAAAAAn2HOPgAAAADkoX7vrcy31xrWuWG+vdbx6KabbrIdO3bYBx98kKX9NR+/Zs2a9u2331rjxo0tP9GzDwAAAADHuc2bN9u9995rp5xyilv7XYkGL7/8cpszZ07EXuOiiy6yXr16WTwbPXq0TZo0yeIBPfsAAAAAcBxT7/O5555rZcuWtREjRljDhg1ddviZM2daz5497ccff8zXTPVHjhxxmeqjsaJC4cKFj7pPmTJlLF7Qsw8AAAAAx7G7777bZYVfvHixdenSxerUqWMNGjSwPn362KJFi9w+GzZssI4dO1rJkiWtdOnSdvXVV9uWLVuCxxg4cKAbpv7f//7XTj75ZBcUd+3a1Xbt2hUc/j5//nzXM67X0k2NDPPmzXP//vTTT61Zs2ZuVMGXX35pBw4csPvuu8+SkpKsaNGidt5559mSJUvcsbTEYbVq1Wzs2LFh56Gh8loN4bfffnP3Ndz+1ltvdcvbqcytWrWyFStWpCvz+PHj3VB7vY688847rsGjWLFidsIJJ1ibNm1sz549wfPo1KlT8BgzZsxwZVNDifa97LLL7JdffrFYQLAPAAAAAMepbdu2uYBVPfglSpRI97iCWAXXCvS1rwL22bNn27p16+yaa64J21dBruayT5s2zd2075NPPukeU5DfokULu+2222zTpk3upqkCnkceecTt+8MPP1ijRo2sb9++9u6779rkyZPtm2++sVq1alm7du1cGRTQX3vttTZlypSw13/99dfdCIUaNWq4+1dddZUlJye7hoRly5ZZ06ZNrXXr1u4Ynp9//tm9znvvvWfLly935dKxb7nlFlcWNUZ07tzZjTjIiBoB1CiydOlSN+VBZbvyyivdNYs2hvEDAAAAwHFKwa4C2dNOOy3TfRTErly50tavXx8M0F999VXX+6/e9rPOOsttU4Cr+eylSpVy92+88Ub33Mcff9z19GuIfPHixa1y5crpXmPw4MF28cUXBwNo9drrWJdcconb9vLLL7tGhldeecUeeughu/76623kyJFuxMFJJ53kXvvNN9+0/v37u/2//PJLN1JBwb5GC8jTTz/tGiPUc3/77bcHh+7rXNT7L2pYOHz4sAvwvUYD9fJnRiMhQk2YMMEd6/vvv7fTTz/doomefQAAAAA4TmXWYx1KPdwK8kN74uvXr+96/fWYR8P3vUBfqlSp4oLtrDjzzDPDRggoZ4B66T2FChWys88+O/h6Gn5fr169YO++RhHotdSbLytWrLDdu3e7ofWaeuDd1GAROsxeAb0X6MsZZ5zhev8V4OtYamTYvn17puVeu3atGwmgxIaaKqBrIGqEiDZ69gEAAADgOFW7dm03Zz4SSfgUkIfScbM6nD2jKQTHot59BfuaAqC/7du3d8G97N692zU2aBh+WmqkyOx1CxQo4EYQLFiwwGbNmmXPPfec/fvf/7avv/7azetPSysWqMFAjQJVq1Z156sefY0YiDZ69gEAAADgOFW+fHk3F/6FF14IJqELpSR36kH//fff3c2jYep6TD38WaVh/Mq0fyynnnqq2/err74KblNPv6YMhL7eddddZ6tWrXLz8TU0X8G/p2nTpm45QWX113z/0FuFChWO+vpqpNCogkGDBrmkfyrL+++/n26/v//+29asWeOmDmg0gK7T0UYB5Dd69v1o8UtKtWGWcOwhOZm6fHQkSwQAAAAgRinQV3CrYfKaO68EeZq3rh5uzZ1XYK9h7QqmR40a5R5TBv8LL7wwbPj9sWiIu3rIlYVfQ+rV0JAR9bbfddddbm6+9tGc/OHDh9vevXutR48eYcf7xz/+4bapEeGKK64IPtamTRuXEFCZ8/VcrTCwceNGmz59ukugl1m5VT7lGWjbtq1bCUD3t27d6gL5tMqVK+dGErz00ktuFIGG7muUQayIarCvN8dbFiGUKo4q3P79++2BBx5wiRa09IJanF588UWrVKlScF9dUFWEuXPnugrTvXt3GzZsWNi6jBq6oQyJq1evdvNM1PKiJRMAAAAAIK8N65x5grdYoPnmSkynRHqKv5SRXvPYtRSegn31dH/44Yd277332gUXXOAyzmvIvIa4Z8eDDz7o4jX1zu/bt8/Nn8+MMvNrSLyS/Gn5PgXnM2fOdAF2KDVAKH7s1q2bWyrPk5CQYJ988okbgn/zzTe7gF2JAVX+0HgyLc27/+KLL1yjRkpKihuir0SAXqLAULoOilW1RKCG7tetW9fGjBljF110kcWChEBWMjLkEV3w0GEcGoKhDIwK3HWBFMSr5UVZGJW98Z577nEX1BvOoecqMYPetBEjRrhKqTdZyzk88cQTbh9VIF34O++8062xqFaaXr16ueOq8SAr9Cbr9Xfu3One/FimD0TytKGWZNsskZ59xBFXd5OTXQuqPudAPKH+Il5RdxHPYq3+qqNSsUfoeu1ARhSCa3SEOqjVqZ1ZvcltHBrVnv3QrIde643mZ2g4iE5Iyyoo0UKrVq3c4xMnTnTDJxYtWmTnnHOOS5igISWfffaZa51R4D9kyBB7+OGHbeDAgW5uxbhx49yFU2uM6PlahuHZZ5/NcrAPAAAAAEA8iZk5+8pW+Nprr7nh9hpyoSQLSsKguRYerf2o+RoLFy50wb7+au5I6DAMBfAaEaAh+02aNHH7hB7D20e9+5lR64puoS0qXuthVrNJRovKp7EaqZaQ2wNFqkhANupuIOY/Y0BGqL+IV9RdxLNYq79eebwbcDReHfHqS0axZm7rdswE+x988IHL5ujNpVfmRPXMhy6LIArs9Zi3T9r5Ft79Y+2jAF7zRELndXg051+ZFzOadqDhObFMFWKnlbaAJVii5eJLJovrYQIRrbs7d7ovu1gYigdkB/UX8Yq6i3gWa/VXHZUqk4Zn6wZkRnXWm86uuqJ6o8z+aZcuVK4CXwT7GrKvpAdamzDa+vXr50YYeNQwoMR+mnYQD3P2EyzFKtr23AX7SUmRLBaQtbqbkOA+Z7HwP2wgO6i/iFfUXcSzWKu/6hRUcKZ52KHJwoHMKLhX0K/6q6z+aefs5zb3Q0zUQmXk17z79957L7hNSfc0tF+9/aG9+1u2bHGPefssXrw47Fh63HvM++ttC91HQXtGvfpSpEgRd0tLb0IsfJEcS0KCuUA/Vwn64uA84T/6H3a8fM6AtKi/iFfUXcSzWKq/KoPK492Ao/Xse3XEqy8Z1ePc1uvofyr+X+I9ZdHs0KFDcJuWeVBLh7Lne9asWeOW2tN6iaK/K1eudFk4PVoLUoG8lnPw9gk9hrePdwwAAAAAAPwmMRaG3yjY13qLocNdtMRAjx493HB6LcWnhH1aH1FBupLzSdu2bV1Qr7UXV6xY4dZd7N+/v/Xs2TPYM68l99atW2d9+/a1H3/80V588UWbOnWq9e7dO2rnDAAAAABAXor6MH4N31dv/S233JLuMS2Pp6ELXbp0cdnxlUVfwbqnQIECNm3aNJd9X40AJUqUcI0GgwcPDu6jZfemT5/ugvvRo0dbtWrVbPz48Sy7BwAAAADwragH++qdz2xpCiUkeOGFF9wtMzVq1LBPPvnkqK9x0UUX2bfffpvrsgIAAAAAEA+iHuwDAAAAgK99fH/+vdblo81vtDy7ErdrufajdfA2btzYRo0ala9li2VRn7MPAAAAAIheIN2pU6d8e7158+aFrVrg3ZR7LTOajj1p0qR8K6Nf0LMPAAAAAMhXWmlNq6h5SpYsmW4frUGvhgAlb0f20bMPAAAAAMjQqlWr7JJLLnHBeKVKldxKaH/99Vewl75w4cL2v//9L7j/8OHD3bLqW7ZsOepxtU/lypWDNx1fvfdly5a1jz76yK26phXWlMw97eiDPXv2WLdu3dxzqlSpYiNHjkx3/JNPPtmGDh0a3E+53nTcrVu3WseOHd22Ro0a2dKlS4PP+fvvv+3aa6+1E0880YoXL24NGza0N954I910gfvuu8+t9la+fHlX9oEDB1osItgHAAAAAKSjefKtWrWyJk2auKB4xowZLoi/+uqrg4Fvr169XAPAzp07XVL0Rx991K1+poaBnNi7d6899dRT7hirV692jQJpPfTQQzZ//nz78MMPbdasWa7R4Ztvvslwdbdzzz3XlatDhw6unAr+b7jhBrf/qaee6u57CeP3799vzZo1c6u5qZHj9ttvd89ZvHhx2HEnT57sVoL7+uuvXeOGVoObPXu2xRqG8QMAAAAA0nn++eddoP/EE08Et02YMMGqV69uP/30k9WpU8f1nivQVWCsAFlLoV9xxRXHPLaWRA/122+/ub+HDh1yy62fccYZGT5v9+7d9sorr9hrr71mrVu3DgbfaY8nl156qd1xxx3u3wMGDLCxY8faWWedZVdddZXb9vDDD7sl3NWAoR569eg/+OCD5rn33ntt5syZNnXqVDv77LOD2zUi4LHHHnP/rl27trtOc+bMsYsvvthiCcE+AAAAACCdFStW2Ny5czOcT//LL7+4YF/D+F9//XUXAGuovHrTs0JD/0uVKhW8X65cOfdXx9OxMqPXPXjwoDVv3jy4TcPp69atm27fRiHH8UYaaGh+2m3Jycku2FeOADVsKLj/888/3escOHDADenP7LiiqQQ6Rqwh2AcAAAAAZNiLfvnll7th9WkpwPUsWLDA/d22bZu7aYj7sdSsWdPNz0+rWLFiLilfJBQqVCj4b++YGW1LTU11f0eMGOEy/2v5PjUK6Dw0TUFBf2bH9Y7jHSOWMGcfAAAAAJBO06ZN3bx5JburVatW2M0L6NXT3rt3b3v55Zddb7uG8edl4Kt59gq2NV/es337djetILe++uorl7xPc/o1jeCUU06JyHGjhWAfAAAAAI5jSq63fPnysNvvv/9uPXv2dD31ylC/ZMkSF9hrDvvNN9/shrzrpsC4Xbt2btvEiRPtu+++yzA7fqRoSkGPHj1ckr7PP//c5QlQtv7ExNyHtrVr13b5BzRS4YcffnDz/Y+1qkAsYxg/AAAAAOSly0dbLFM2eyXiC6WAWhnx1dutRHZt27Z189c1L799+/YuuB4yZIhLrDdt2rTg0P6XXnrJNQ5o/8yS7OWWhtt7Uww07/+BBx5wDRa51b9/f1u3bp1rvNA8fSUd1JJ/kTh2NCQEvHUGkKmUlBQrU6aMe5NLly5tsUxDZpKnDbUk22aJCQHffiHBf1zdTU52y6tEomUWyE/UX8Qr6i7iWazVXy3btn79ejcXvWjRotEuDmKYQvDDhw9bwYIFXQNKZvUmt3Fo9D8VAAAAAAAgogj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAgAgh/zlipb4Q7AMAAABALhUqVMj93bt3b7SLgjhy8OBB97dAgQIRP3bBiB8RAAAAAI4zCtbKli3rlgMUrdOekJAQ7WIhhpfe05KRW7dudXVFy/BFGsE+AAAAAERA5cqV3V8v4AcyC/ZTU1NdsK9GopNOOilPGoYI9gEAAAAgAhSwValSxZKSkuzQoUPRLg5ilAL9v//+2ypUqGBFihRxQX9eINgHAAAAgAhSb21ezMGGf4L9QoUK5WmgLyToAwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnoh7s//nnn3bDDTfYCSecYMWKFbOGDRva0qVLg48HAgEbMGCAValSxT3epk0bW7t2bdgxtm3bZtdff72VLl3aypYtaz169LDdu3eH7fPdd9/Z+eefb0WLFrXq1avb8OHD8+0cAQAAAAA4boL97du327nnnmuFChWyTz/91L7//nsbOXKklStXLriPgvIxY8bYuHHj7Ouvv7YSJUpYu3btbP/+/cF9FOivXr3aZs+ebdOmTbMvvvjCbr/99uDjKSkp1rZtW6tRo4YtW7bMRowYYQMHDrSXXnop388ZAAAAAIC8VtCi6KmnnnK97BMnTgxuq1mzZliv/qhRo6x///7WsWNHt+3VV1+1SpUq2QcffGBdu3a1H374wWbMmGFLliyxM8880+3z3HPP2aWXXmpPP/20Va1a1V5//XU7ePCgTZgwwQoXLmwNGjSw5cuX2zPPPBPWKAAAAAAAgB9ENdj/6KOPXC/9VVddZfPnz7cTTzzR7r77brvtttvc4+vXr7fNmze7ofueMmXKWPPmzW3hwoUu2NdfDd33An3R/omJiW4kwJVXXun2ueCCC1yg79HrqrFBowtCRxLIgQMH3C10ZICkpqa6WyxT+QIBs1RLyO2BIlUkIBt1NxDznzEgI9RfxCvqLuIZ9Rd+r7upuazbUQ32161bZ2PHjrU+ffrYv/71L9c7f99997mgvHv37i7QF/Xkh9J97zH9TUpKCnu8YMGCVr58+bB9QkcMhB5Tj6UN9ocNG2aDBg1KV96tW7eGTR+IRaoQO620BSzBEi2Q8wMlJ0eyWEDW6u7One6LT411QDyh/iJeUXcRz6i/8Hvd3bVrV/wG+zpJ9cg/8cQT7n6TJk1s1apVbn6+gv1o6devn2uACO3Z13SDihUruiSAsUzXNMFSrKJtz12wn6YBBciXupuQ4D5n/A8b8Yb6i3hF3UU8o/7C73W3aNGi8RvsK8N+/fr1w7bVq1fP3n33XffvypUru79btmxx+3p0v3HjxsF9ktP0Qh8+fNhl6Peer796TijvvrdPqCJFirhbWnoj4uGLJCFBmRcDlpiQi2A/Ds4T/qMvvXj5nAFpUX8Rr6i7iGfUX/i57ibmsl5H9VOhTPxr1qwJ2/bTTz+5rPmiofcKxufMmRPWy665+C1atHD39XfHjh0uy77n888/d60lmtvv7aMM/YcOHQruo8z9devWTTeEHwAAAACAeBfVYL937962aNEiN4z/559/tilTprjl8Hr27Bls7ejVq5cNHTrUJfNbuXKldevWzWXY79SpU3AkQPv27V1Sv8WLF9tXX31l99xzj0vep/3kuuuuc3kAevTo4Zboe+utt2z06NFhQ/UBAAAAAPCLqA7jP+uss+z99993c+QHDx7sevK11N71118f3Kdv3762Z88et0SeevDPO+88t9Re6PwFLa2nAL9169ZuqEOXLl1szJgxYRn8Z82a5RoRmjVrZhUqVLABAwaw7B4AAAAAwJcSAkoBiKPS1AE1GChjYjwk6EueNtSSbFvu5uxfPjqSxQKyVneTk93qGsy7Q7yh/iJeUXcRz6i/8HvdTcllHMqnAgAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwmagG+wMHDrSEhISw22mnnRZ8fP/+/dazZ0874YQTrGTJktalSxfbsmVL2DE2bNhgHTp0sOLFi1tSUpI99NBDdvjw4bB95s2bZ02bNrUiRYpYrVq1bNKkSfl2jgAAAAAAHHc9+w0aNLBNmzYFb19++WXwsd69e9vHH39sb7/9ts2fP982btxonTt3Dj5+5MgRF+gfPHjQFixYYJMnT3aB/IABA4L7rF+/3u3TsmVLW758ufXq1ctuvfVWmzlzZr6fKwAAAAAA+aFg1AtQsKBVrlw53fadO3faK6+8YlOmTLFWrVq5bRMnTrR69erZokWL7JxzzrFZs2bZ999/b5999plVqlTJGjdubEOGDLGHH37YjRooXLiwjRs3zmrWrGkjR450x9Dz1aDw7LPPWrt27fL9fAEAAAAA8H2wv3btWqtataoVLVrUWrRoYcOGDbOTTjrJli1bZocOHbI2bdoE99UQfz22cOFCF+zrb8OGDV2g71EAf9ddd9nq1autSZMmbp/QY3j7qIc/MwcOHHA3T0pKivubmprqbrFM5QsEzFItIbcHilSRgGzU3UDMf8aAjFB/Ea+ou4hn1F/4ve6m5rJuRzXYb968uRt2X7duXTeEf9CgQXb++efbqlWrbPPmza5nvmzZsmHPUWCvx0R/QwN973HvsaPtowB+3759VqxYsXTlUoODypLW1q1bXR6BWKYKsdNKW8ASLNECOT9QcnIkiwVkre7u3Om++BIToz7DCMgW6i/iFXUX8Yz6C7/X3V27dsVvsH/JJZcE/92oUSMX/NeoUcOmTp2aYRCeX/r162d9+vQJ3lfDQPXq1a1ixYpWunRpi/WKk2ApVtG25y7YT0qKZLGArNXdhAT3OeN/2Ig31F/EK+ou4hn1F36vu0WLFo3vYfyh1Itfp04d+/nnn+3iiy92ifd27NgR1ruvbPzeHH/9Xbx4cdgxvGz9ofukzeCv+wraM2tQUNZ+3dLSGxEPXyQJCcq8GLDEhFwE+3FwnvAffenFy+cMSIv6i3hF3UU8o/7Cz3U3MZf1OqY+Fbt377ZffvnFqlSpYs2aNbNChQrZnDlzgo+vWbPGLbWnuf2ivytXrrTkkCHns2fPdoF8/fr1g/uEHsPbxzsGAAAAAAB+E9Vg/8EHH3RL6v36669u6bwrr7zSChQoYNdee62VKVPGevTo4YbTz5071yXsu/nmm12QruR80rZtWxfU33jjjbZixQq3nF7//v2tZ8+ewZ75O++809atW2d9+/a1H3/80V588UU3TUDL+gEAAAAA4EdRHcb/xx9/uMD+77//dvMVzjvvPLesnv4tWh5PQxe6dOnisuMri76CdY8aBqZNm+ay76sRoESJEta9e3cbPHhwcB8tuzd9+nQX3I8ePdqqVatm48ePZ9k9AAAAAIBvJQSUAhBHpQR9GmmgjInxkKAvedpQS7JtuZuzf/noSBYLyFrdTU62pKQk5t0h7lB/Ea+ou4hn1F/4ve6m5DIO5VMBAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD6To2B/3bp1kS8JAAAAAACIXrBfq1Yta9mypb322mu2f//+yJQEAAAAAABEL9j/5ptvrFGjRtanTx+rXLmy3XHHHbZ48eLIlAgAAAAAAOR/sN+4cWMbPXq0bdy40SZMmGCbNm2y8847z04//XR75plnbOvWrbkrFQAAAAAAiE6CvoIFC1rnzp3t7bfftqeeesp+/vlne/DBB6169erWrVs31wgAAAAAAADiKNhfunSp3X333ValShXXo69A/5dffrHZs2e7Xv+OHTtGrqQAAAAAACBLCloOKLCfOHGirVmzxi699FJ79dVX3d/ExP9rO6hZs6ZNmjTJTj755JwcHgAAAAAA5HewP3bsWLvlllvspptucr36GUlKSrJXXnklN2UDAAAAAAD5FeyvXbv2mPsULlzYunfvnpPDAwAAAACA/J6zryH8SsqXlrZNnjw5N+UBAAAAAADRCPaHDRtmFSpUyHDo/hNPPJHbMgEAAAAAgPwO9jds2OCS8KVVo0YN91hOPPnkk5aQkGC9evUKbtu/f7/17NnTTjjhBCtZsqR16dLFtmzZkq4sHTp0sOLFi7vGhoceesgOHz4cts+8efOsadOmVqRIEatVq5ZLHggAAAAAgF/lKNhXUP3dd9+l275ixQoXmGfXkiVL7D//+Y81atQobHvv3r3t448/dtMD5s+f75bz69y5c/DxI0eOuED/4MGDtmDBAjeFQIH8gAEDgvusX7/e7dOyZUtbvny5a0y49dZbbebMmdkuJwAAAAAAvg32r732Wrvvvvts7ty5LuDW7fPPP7f777/funbtmq1j7d69266//np7+eWXrVy5csHtO3fudNn8tcxfq1atrFmzZi5XgIL6RYsWuX1mzZpl33//vb322mvWuHFju+SSS2zIkCH2wgsvuAYAGTdunBuFMHLkSKtXr57dc8899s9//tOeffbZnJw6AAAAAAD+zMavgPrXX3+11q1bW8GC/3eI1NRU69atW7bn7GuYvnre27RpY0OHDg1uX7ZsmR06dMht95x22ml20kkn2cKFC+2cc85xfxs2bGiVKlUK7tOuXTu76667bPXq1dakSRO3T+gxvH1CpwukdeDAAXfzpKSkBM9Rt1im8gUCZqmWkNsDRapIQDbqbiDmP2NARqi/iFfUXcQz6i/8XndTc1m3cxTsa1m9t956ywX9GrpfrFgxF3Rrzn52vPnmm/bNN9+4Yfxpbd682b1O2bJlw7YrsNdj3j6hgb73uPfY0fZRAL9v3z5X9owSEA4aNCjd9q1bt7o8ArFMFWKnlbaAJViiBXJ+oOTkSBYLyFrd3bnTffElJuZo0BEQNdRfxCvqLuIZ9Rd+r7u7du3K/2DfU6dOHXfLid9//90N+589e7YVLVrUYkm/fv2sT58+wftqGKhevbpVrFjRSpcubbFecRIsxSra9twF+0lJkSwWkLW6m5DgPmf8DxvxhvqLeEXdRTyj/sLvdbdoLuPkHAX7mqOvRHhz5syx5OTkdMMLNH//WDRMX89VlvzQ437xxRf2/PPPuwR6mne/Y8eOsN59ZeOvXLmy+7f+Ll68OOy4Xrb+0H3SZvDXfQXtGfXqi7L265aW3oh4+CJJSFAyhoAlJuQi2I+D84T/6EsvXj5nQFrUX8Qr6i7iGfUXfq67ibms1zkK9tUjr2Bfc+1PP/10V9Ds0nz/lStXhm27+eab3bz8hx9+2PWkFypUyDUoaMk9WbNmjVtqr0WLFu6+/j7++OOu0UArBIhGCiiQr1+/fnCfTz75JOx1tI93DAAAAAAA/CZHwb7m2k+dOtUuvfTSHL9wqVKlXENBqBIlSril+7ztPXr0cMPpy5cv7wL4e++91wXpSs4nbdu2dUH9jTfeaMOHD3fz8/v37++S/nk983feeacbKdC3b1+75ZZb3KgDlX369Ok5LjsAAAAAALEsxwn6atWqZXlNy+Np6IJ69pUdX1n0X3zxxeDjBQoUsGnTprns+2oEUGNB9+7dbfDgwcF9tOyeAvvevXvb6NGjrVq1ajZ+/Hh3LAAAAAAA/CghoBSA2aQ169etW+d6zHMyhD/eKEFfmTJlXMbEeEjQlzxtqCXZttzN2b98dCSLBWSt7v6/KTnMu0O8of4iXlF3Ec+ov/B73U3JZRyao579L7/80ubOnWuffvqpNWjQwM2tD/Xee+/l5LAAAAAAACACchTsKzv+lVdeGYnXBwAAAAAAsRDsT5w4MdLlAAAAAAAAEZLjyS2HDx+2zz77zP7zn//Yrl273LaNGzfa7t27I1U2AAAAAACQXz37v/32m7Vv396tea8s+RdffLFbSu+pp55y98eNG5eTwwIAAAAAgGj17N9///125pln2vbt261YsWLB7ZrHP2fOnEiUCwAAAAAA5GfP/v/+9z9bsGCBFS5cOGz7ySefbH/++WdOywIAAAAAAKLVs691AY8cOZJu+x9//OGG8wMAAAAAgDgL9tu2bWujRo0K3k9ISHCJ+R577DG79NJLI1k+AAAAAACQH8P4R44cae3atbP69evb/v377brrrrO1a9dahQoV7I033sjJIQEAAAAAQDSD/WrVqtmKFSvszTfftO+++8716vfo0cOuv/76sIR9AAAAAAAgToJ998SCBe2GG26IbGkAAAAAAEB0gv1XX331qI9369Ytp+UBAAAAAADRCPbvv//+sPuHDh2yvXv3uqX4ihcvTrAPAAAAAEC8ZePfvn172E1z9tesWWPnnXceCfoAAAAAAIjHYD8jtWvXtieffDJdrz8AAAAAAIjTYN9L2rdx48ZIHhIAAAAAAOTHnP2PPvoo7H4gELBNmzbZ888/b+eee25ODgkAAAAAAKIZ7Hfq1CnsfkJCglWsWNFatWplI0eOjFTZAAAAAABAfgX7qampOXkaAAAAAACItzn7AAAAAAAgTnv2+/Tpk+V9n3nmmZy8BAAAAAAAyM9g/9tvv3W3Q4cOWd26dd22n376yQoUKGBNmzYNm8sPAAAAAADiINi//PLLrVSpUjZ58mQrV66c27Z9+3a7+eab7fzzz7cHHngg0uUEAAAAAAB5OWdfGfeHDRsWDPRF/x46dCjZ+AEAAAAAiMdgPyUlxbZu3Zpuu7bt2rUrEuUCAAAAAAD5GexfeeWVbsj+e++9Z3/88Ye7vfvuu9ajRw/r3LlzTssCAAAAAACiNWd/3Lhx9uCDD9p1113nkvS5AxUs6IL9ESNGRKJcAAAAAAAgP4P94sWL24svvugC+19++cVtO/XUU61EiRI5LQcAAAAAAIjmMH7Ppk2b3K127dou0A8EApEqFwAAAAAAyM9g/++//7bWrVtbnTp17NJLL3UBv2gYP8vuAQAAAAAQh8F+7969rVChQrZhwwY3pN9zzTXX2IwZMyJZPgAAAAAAkB9z9mfNmmUzZ860atWqhW3XcP7ffvstJ4cEAAAAAADR7Nnfs2dPWI++Z9u2bVakSJFIlAsAAAAAAORnsH/++efbq6++GryfkJBgqampNnz4cGvZsmVOywIAAAAAAKI1jF9BvRL0LV261A4ePGh9+/a11atXu579r776KhLlAgAAAAAA+dmzf/rpp9tPP/1k5513nnXs2NEN6+/cubN9++23duqpp+a0LAAAAAAAIBo9+4cOHbL27dvbuHHj7N///nckygAAAAAAAKLZs68l97777rtIlgEAAAAAAER7GP8NN9xgr7zySiTLAQAAAAAAopmg7/DhwzZhwgT77LPPrFmzZlaiRImwx5955plIlQ8AAAAAAORlsL9u3To7+eSTbdWqVda0aVO3TYn6QmkZPgAAAAAAECfBfu3atW3Tpk02d+5cd/+aa66xMWPGWKVKlfKqfAAAAAAAIC/n7AcCgbD7n376qVt2DwAAAAAAxHmCvsyCfwAAAAAAEGfBvubjp52Tzxx9AAAAAADieM6+evJvuukmK1KkiLu/f/9+u/POO9Nl43/vvfciW0oAAAAAAJA3wX737t3D7t9www3ZeToAAAAAAIi1YH/ixIl5VxIAAAAAABD9BH0AAAAAACD2EOwDAAAAAOAzBPsAAAAAAPhMVIP9sWPHWqNGjax06dLu1qJFC/v000+Djyvbf8+ePe2EE06wkiVLWpcuXWzLli1hx9iwYYN16NDBihcvbklJSfbQQw/Z4cOHw/aZN2+eNW3a1K0iUKtWLZs0aVK+nSMAAAAAAMdVsF+tWjV78sknbdmyZbZ06VJr1aqVdezY0VavXu0e7927t3388cf29ttv2/z5823jxo3WuXPn4POPHDniAv2DBw/aggULbPLkyS6QHzBgQHCf9evXu31atmxpy5cvt169etmtt95qM2fOjMo5AwAAAACQ1xICgUDAYkj58uVtxIgR9s9//tMqVqxoU6ZMcf+WH3/80erVq2cLFy60c845x40CuOyyy1wjQKVKldw+48aNs4cffti2bt1qhQsXdv+ePn26rVq1KvgaXbt2tR07dtiMGTMyLMOBAwfczZOSkmLVq1e37du3uxEIsSw1NdW2Tn/CKtp2S0zIxVt72TORLBaQtbq7dav73CcmMsMI8YX6i3hF3UU8o/7C73U3JSXFypUrZzt37sxRHJqtpffyknrp1YO/Z88eN5xfvf2HDh2yNm3aBPc57bTT7KSTTgoG+/rbsGHDYKAv7dq1s7vuusuNDmjSpInbJ/QY3j7q4c/MsGHDbNCgQem26w3R1IJYrzg7rbQFLMESLRfBfnJyJIsFZK3u7txpan/kf9iIN9RfxCvqLuIZ9Rd+r7u7du3K1etEPdhfuXKlC+4VRGte/vvvv2/169d3Q+7VM1+2bNmw/RXYb9682f1bf0MDfe9x77Gj7aNWkn379lmxYsXSlalfv37Wp0+fdD37anmJh579BEv5v5793AT7SUmRLBaQtbqbkEDrPOIS9RfxirqLeEb9hd/rbtGiReM72K9bt64L7NWy8c4771j37t3d/PxoUiI/3dLSGxEPXyQJCUrGEMjdMP44OE/4j7704uVzBqRF/UW8ou4inlF/4ee6m5jLeh31YF+998qQL82aNbMlS5bY6NGj7ZprrnGJ9zS3PrR3X9n4K1eu7P6tv4sXLw47npetP3SftBn8dV899Bn16gMAAAAAEO8SY3FIg5LjKfAvVKiQzZkzJ/jYmjVr3FJ7GvYv+qtpAMkh88tnz57tAnlNBfD2CT2Gt493DAAAAAAA/CaqPfuaG3/JJZe4pHtKPqDM+/PmzXPL4pUpU8Z69Ojh5s4rQ78C+HvvvdcF6UrOJ23btnVB/Y033mjDhw938/P79+9vPXv2DA7Dv/POO+3555+3vn372i233GKff/65TZ061WXoBwAAAADAj6Ia7KtHvlu3brZp0yYX3Ddq1MgF+hdffLF7/Nlnn3XzFLp06eJ6+5VF/8UXXww+v0CBAjZt2jSXfV+NACVKlHBz/gcPHhzcp2bNmi6w7927t5seUK1aNRs/frw7FgAAAAAAfpQQUL5/HJWy8asxIqfrG+b3NIjkaUMtybblLkHf5aMjWSwga3U3OdmSkpJIsoO4Q/1FvKLuIp5Rf+H3upuSyziUTwUAAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPhMwWgXAJG3Zstu+3XfNkuwQLae17xm+TwrEwAAAAAg/9CzDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgM1EN9ocNG2ZnnXWWlSpVypKSkqxTp062Zs2asH32799vPXv2tBNOOMFKlixpXbp0sS1btoTts2HDBuvQoYMVL17cHeehhx6yw4cPh+0zb948a9q0qRUpUsRq1aplkyZNypdzBAAAAADguAr258+f7wL5RYsW2ezZs+3QoUPWtm1b27NnT3Cf3r1728cff2xvv/2223/jxo3WuXPn4ONHjhxxgf7BgwdtwYIFNnnyZBfIDxgwILjP+vXr3T4tW7a05cuXW69evezWW2+1mTNn5vs5AwAAAACQ1wpaFM2YMSPsvoJ09cwvW7bMLrjgAtu5c6e98sorNmXKFGvVqpXbZ+LEiVavXj3XQHDOOefYrFmz7Pvvv7fPPvvMKlWqZI0bN7YhQ4bYww8/bAMHDrTChQvbuHHjrGbNmjZy5Eh3DD3/yy+/tGeffdbatWsXlXMHAAAAAMCXwX5aCu6lfPny7q+CfvX2t2nTJrjPaaedZieddJItXLjQBfv627BhQxfoexTA33XXXbZ69Wpr0qSJ2yf0GN4+6uHPyIEDB9zNk5KS4v6mpqa6WyxT+QJmFrCE7D83EPKcGD9P+I+ru4FAzH/GgIxQfxGvqLuIZ9Rf+L3upuaybsdMsK8TUfB97rnn2umnn+62bd682fXMly1bNmxfBfZ6zNsnNND3HvceO9o+CuL37dtnxYoVS5dLYNCgQenKuHXrVpdDIJbpOh4uXM4SXLCvsD/rkq1kyJ3kyBcOOEbdVYOfvvgSE8kdivhC/UW8ou4inlF/4fe6u2vXLn8E+5q7v2rVKje8Ptr69etnffr0Cd5Xo0D16tWtYsWKVrp0aYv1ivPTwe1WeN8WS8hmsJ9k5UPuJEW+cMAx6m5CQoL7nPE/bMQb6i/iFXUX8Yz6C7/X3aJFi8Z/sH/PPffYtGnT7IsvvrBq1aoFt1euXNkl3tuxY0dY776y8esxb5/FixeHHc/L1h+6T9oM/rqvwD1tr74oY79uaemNiIcvEvXpK9DPbrCfmBCyfxycJ/xHX3rx8jkD0qL+Il5RdxHPqL/wc91NzGW9juqnQsMWFOi///779vnnn7skeqGaNWtmhQoVsjlz5gS3aWk+LbXXokULd19/V65cackhw86V2V+BfP369YP7hB7D28c7BgAAAAAAflIw2kP3lWn/ww8/tFKlSgXn2JcpU8b1uOtvjx493JB6Je1TAH/vvfe6IF3J+URL9Smov/HGG2348OHuGP3793fH9nrn77zzTnv++eetb9++dsstt7iGhalTp9r06dOjefoAAAAAAOSJqPbsjx071iUmuOiii6xKlSrB21tvvRXcR8vjXXbZZdalSxe3HJ+G5L/33nvBxwsUKOCmAOivGgFuuOEG69atmw0ePDi4j0YMKLBXb/4ZZ5zhluAbP348y+4BAAAAAHypYLSH8R+LkhK88MIL7paZGjVq2CeffHLU46hB4dtvv81ROQEAAAAAiCdksgAAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfCaqwf4XX3xhl19+uVWtWtUSEhLsgw8+CHs8EAjYgAEDrEqVKlasWDFr06aNrV27Nmyfbdu22fXXX2+lS5e2smXLWo8ePWz37t1h+3z33Xd2/vnnW9GiRa169eo2fPjwfDk/AAAAAACOu2B/z549dsYZZ9gLL7yQ4eMKyseMGWPjxo2zr7/+2kqUKGHt2rWz/fv3B/dRoL969WqbPXu2TZs2zTUg3H777cHHU1JSrG3btlajRg1btmyZjRgxwgYOHGgvvfRSvpwjAAAAAAD5raBF0SWXXOJuGVGv/qhRo6x///7WsWNHt+3VV1+1SpUquREAXbt2tR9++MFmzJhhS5YssTPPPNPt89xzz9mll15qTz/9tBsx8Prrr9vBgwdtwoQJVrhwYWvQoIEtX77cnnnmmbBGAQAAAAAA/CKqwf7RrF+/3jZv3uyG7nvKlCljzZs3t4ULF7pgX381dN8L9EX7JyYmupEAV155pdvnggsucIG+R6MDnnrqKdu+fbuVK1cu3WsfOHDA3UJHB0hqaqq7xTKVL6DGEkvI/nMDIc+J8fOE/7i6GwjE/GcMyAj1F/GKuot4Rv2F3+tuai7rdswG+wr0RT35oXTfe0x/k5KSwh4vWLCglS9fPmyfmjVrpjuG91hGwf6wYcNs0KBB6bZv3bo1bApBLFKFOFy4nCW4YF9hf9YlW8mQO8mRLxxwjLq7c+dO98WnBjsgnlB/Ea+ou4hn1F/4ve7u2rXLn8F+NPXr18/69OkT1rOvxH4VK1Z0iQBjveL8dHC7Fd63xRKyGewnWfmQO+GNKEB+1F0l6tTnjP9hI95QfxGvqLuIZ9RfxKus1l0lmPdlsF+5cmX3d8uWLS4bv0f3GzduHNwnOU0P9OHDh12Gfu/5+qvnhPLue/ukVaRIEXdLS29EPHyRqE9fgX52g/3EhJD94+A84T/60ouXzxmQFvUX8Yq6i3hG/YWf625iLut1zH4qNPRewficOXPCetg1F79Fixbuvv7u2LHDZdn3fP75566lRHP7vX2Uof/QoUPBfZS5v27duhkO4QcAAAAAIN5FNdjfvXu3y4yvm5eUT//esGGDa+no1auXDR061D766CNbuXKldevWzWXY79Spk9u/Xr161r59e7vtttts8eLF9tVXX9k999zjkvdpP7nuuutccr4ePXq4JfreeustGz16dNgwfQAAAAAA/CSqw/iXLl1qLVu2DN73AvDu3bvbpEmTrG/fvrZnzx63RJ568M877zy31F7o3AUtracAv3Xr1m6YQ5cuXWzMmDFhGfxnzZplPXv2tGbNmlmFChVswIABLLsHAAAAAPCtqAb7F110kctAmBn17g8ePNjdMqPM+1OmTDnq6zRq1Mj+97//5aqsAAAAAADEi5idsw8AAAAAAHKGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGeOq2D/hRdesJNPPtmKFi1qzZs3t8WLF0e7SAAAAAAARNxxE+y/9dZb1qdPH3vsscfsm2++sTPOOMPatWtnycnJ0S4aAAAAAAARVdCOE88884zddtttdvPNN7v748aNs+nTp9uECRPskUceiXbxAAA+0u+9lTl+7rDODSNaFgAAcHw6LoL9gwcP2rJly6xfv37BbYmJidamTRtbuHBhuv0PHDjgbp6dO3e6vzt27LDU1FSLZSrf7n0H7ND+Q5ZggWw9d8eegyF3dkS+cMAx6m5KSooVLlzYfT6BaBs87fts7B2wMrbPdloxM0twWzpsHJOj150z3GLK9Kr35foYOb0Wfi1HJETuXNLX3dwacFn9fPrcHb9yc439hN8O8HvdTUlJcX8DgezFdZ6EQE6fGUc2btxoJ554oi1YsMBatGgR3N63b1+bP3++ff3112H7Dxw40AYNGhSFkgIAAAAA8P/7/fffrVq1apZdx0XPfnZpBIDm94e2vGzbts1OOOEES0iITKt3XlHrT/Xq1V2FKF26dLSLA2QZdRfxjPqLeEXdRTyj/sLvdTcQCNiuXbusatWqOXqd4yLYr1ChghUoUMC2bNkStl33K1eunG7/IkWKuFuosmXLWjxRpeFLD/GIuot4Rv1FvKLuIp5Rf+HnulumTJkcH/+4mNyiuRDNmjWzOXPmhPXW637osH4AAAAAAPzguOjZFw3L7969u5155pl29tln26hRo2zPnj3B7PwAAAAAAPjFcRPsX3PNNbZ161YbMGCAbd682Ro3bmwzZsywSpUqmZ9o+sFjjz2WbhoCEOuou4hn1F/EK+ou4hn1F/GqSD7V3eMiGz8AAAAAAMeT42LOPgAAAAAAxxOCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2Y9wLL7xgJ598shUtWtSaN29uixcvPur+b7/9tp122mlu/4YNG9onn3wS9rjyMWpFgipVqlixYsWsTZs2tnbt2jw+CxyvIl1/b7rpJktISAi7tW/fPo/PAsej7NTd1atXW5cuXdz+qpNa2jW3xwRiqf4OHDgw3XevvquBaNbdl19+2c4//3wrV66cu+k3bdr9+d2LeK6/kfjdS7Afw9566y3r06ePW5bhm2++sTPOOMPatWtnycnJGe6/YMECu/baa61Hjx727bffWqdOndxt1apVwX2GDx9uY8aMsXHjxtnXX39tJUqUcMfcv39/Pp4Zjgd5UX9FX3KbNm0K3t544418OiMcL7Jbd/fu3WunnHKKPfnkk1a5cuWIHBOIpforDRo0CPvu/fLLL/PwLHA8ym7dnTdvnvvdMHfuXFu4cKFVr17d2rZta3/++WdwH373Ip7rb0R+92rpPcSms88+O9CzZ8/g/SNHjgSqVq0aGDZsWIb7X3311YEOHTqEbWvevHngjjvucP9OTU0NVK5cOTBixIjg4zt27AgUKVIk8MYbb+TZeeD4FOn6K927dw907NgxD0sNZL/uhqpRo0bg2WefjegxgWjX38ceeyxwxhlnRLysQCS/Jw8fPhwoVapUYPLkye4+v3sRz/U3Ur976dmPUQcPHrRly5a5IR2exMREd1+tPxnR9tD9RS1K3v7r16+3zZs3h+1TpkwZN8wks2MCsVJ/Q1tCk5KSrG7dunbXXXfZ33//nUdngeNRTupuNI4J5Hdd09DnqlWrulEA119/vW3YsCECJQYiV3c1SuXQoUNWvnx5d5/fvYjn+hup370E+zHqr7/+siNHjlilSpXCtuu+vrgyou1H29/7m51jArFSf72hTK+++qrNmTPHnnrqKZs/f75dcskl7rWAaNXdaBwTyM+6puBo0qRJNmPGDBs7dqwLojTXdNeuXREoNRCZuvvwww+7Bikv4OJ3L+K5/kbqd2/BbJwHAERV165dg/9WAr9GjRrZqaee6lo9W7duHdWyAYBf6celR9+7Cv5r1KhhU6dOdXlWgGhTzok333zT/R5QcjTAD/W3awR+99KzH6MqVKhgBQoUsC1btoRt1/3MEuho+9H29/5m55hArNTfjGg4qV7r559/jlDJcbzLSd2NxjGBaNa1smXLWp06dfjuRUzU3aefftoFS7NmzXLBkIffvYjn+hup370E+zGqcOHC1qxZMzdsw5Oamurut2jRIsPnaHvo/jJ79uzg/jVr1nQVLnSflJQUl500s2MCsVJ/M/LHH3+4uUtaUgeIVt2NxjGBaNa13bt32y+//MJ3L6Jed5Vtf8iQIW6KyZlnnhn2GL97Ec/1N2K/e3OV3g956s0333QZQydNmhT4/vvvA7fffnugbNmygc2bN7vHb7zxxsAjjzwS3P+rr74KFCxYMPD0008HfvjhB5c9t1ChQoGVK1cG93nyySfdMT788MPAd9995zI81qxZM7Bv376onCP8K9L1d9euXYEHH3wwsHDhwsD69esDn332WaBp06aB2rVrB/bv3x+184T/ZLfuHjhwIPDtt9+6W5UqVVw91b/Xrl2b5WMCsVx/H3jggcC8efPcd6++q9u0aROoUKFCIDk5OSrnCH/Kbt3Vb9rChQsH3nnnncCmTZuCN/1eCN2H372Ix/obqd+9BPsx7rnnngucdNJJrjJoSYdFixYFH7vwwgvdkgyhpk6dGqhTp47bv0GDBoHp06eHPa5lSB599NFApUqVXIVs3bp1YM2aNfl2Pji+RLL+7t27N9C2bdtAxYoVXSOAloi67bbbCJYQ9bqr/wmr7TztTftl9ZhALNffa665xjUE6Hgnnniiu//zzz/n+3nB/7JTd/U7IKO6q84CD797Ea/1N1K/exP0n6yPAwAAAAAAALGOOfsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAgZv3666+WkJBgy5cvj3ZRAACIKwT7AADksZtuusk6deqUbvu8efNcILtjx44sH+uiiy6yXr16RaRcL7/8sp1xxhlWsmRJK1u2rDVp0sSGDRtmfrBixQq74oorLCkpyYoWLWonn3yyXXPNNZacnBztogEAkC8K5s/LAACAWDJhwgTXaDBmzBi78MIL7cCBA/bdd9/ZqlWrLN5t3brVWrdubZdddpnNnDnTNWRohMBHH31ke/bsybPXPXTokBUqVCjPjg8AQHbQsw8AQIz4+++/7dprr7UTTzzRihcvbg0bNrQ33ngjbITA/PnzbfTo0W5EgG4KYkVB+iWXXOJ66StVqmQ33nij/fXXX5m+lgLfq6++2nr06GG1atWyBg0auNd+/PHH041IGDRokFWsWNFKly5td955px08eDC4T2pqqhsNULNmTStWrJgbKfDOO++EvdaxyqZjDB8+3JWjSJEidtJJJ4WVQ9atW2ctW7Z010WvsXDhwkzP7auvvrKdO3fa+PHj3WgFlU3PffbZZ92/PatXr3YNAjqvUqVK2fnnn2+//PJLsEyDBw+2atWquTI1btzYZsyYkW56wVtvveUaSzR64PXXX3eP6XXr1avntp122mn24osvZlpWAADyCsE+AAAxYv/+/dasWTObPn26C5Bvv/12FxgvXrzYPa4gv0WLFnbbbbfZpk2b3K169epuGkCrVq1cYLt06VIXlG7ZssUF85mpXLmyLVq0yH777bejlmnOnDn2ww8/uCkHanh47733XPDvUaD/6quv2rhx41zw3Lt3b7vhhhtco4RkpWz9+vWzJ5980h599FH7/vvvbcqUKa5RINS///1ve/DBB93c/Tp16riGicOHD2d6bnrs/ffft0AgkOE+f/75p11wwQUukP/8889t2bJldssttwSPqWs9cuRIe/rpp92Ih3bt2rlpAWvXrg07ziOPPGL333+/u0baRwH/gAEDXGOFtj3xxBPuvCZPnnzU6wwAQMQFAABAnurevXugQIECgRIlSoTdihYtqkg0sH379kyf26FDh8ADDzwQvH/hhRcG7r///rB9hgwZEmjbtm3Ytt9//90de82aNRked+PGjYFzzjnH7VOnTh1Xxrfeeitw5MiRsHKXL18+sGfPnuC2sWPHBkqWLOn2279/f6B48eKBBQsWhB27R48egWuvvTZLZUtJSQkUKVIk8PLLL2dYzvXr17t9x48fH9y2evVqt+2HH37I9Lr961//ChQsWNCVv3379oHhw4cHNm/eHHy8X79+gZo1awYOHjyY4fOrVq0aePzxx8O2nXXWWYG77747rFyjRo0K2+fUU08NTJkyJWybrkGLFi0yLSsAAHmBOfsAAOQDDSMfO3Zs2Lavv/7a9YJ7jhw54nqCp06d6nqeNVxec+k1dP1Yyejmzp3rhsmnpWHp6glPq0qVKm4ovEYQfPHFF7ZgwQLr3r27G4Ku3vfExP8b/Kch86Gvr5EFu3fvtt9//9393bt3r1188cVhx1a51ZOflbKp51/nqDn2R9OoUaOwsouS7WmYfEbUs96nTx/Xa6/rrJEHurY6V02P0AgBDdvPaI59SkqKbdy40c4999yw7bqv8wl15plnBv+tfAA6J02N0OgLj0YLlClT5qjnBwBApBHsAwCQD0qUKOHmpIf6448/wu6PGDHCDR8fNWqUC0j1HCXRC50jnxEF3Zdffrk99dRT6R7zAuPMnH766e529913u/n4CoA1BF+NE8ei1xVNO1CegVAaHp+VsmkuflaEBuWaK+/Nqz+aE044wa666ip3U6CvBggNy9eQeuUXiAS9R2mvh1Y5aN68edh+BQoUiMjrAQCQVQT7AADECCWW69ixY7C3X8HsTz/9ZPXr1w/uU7hwYTcCIFTTpk3t3XffdcvLFSyY8/+1e68TmrFePdn79u0LBsea569eeuUKKF++vAvqN2zY4JLUZeRYZatdu7Y7tnID3HrrrZZXdN1OPfXU4LlppICC/owy6CthX9WqVd37EXpeun/22Wdn+hrKM6DnqQHj+uuvz7NzAQAgK0jQBwBAjFDgO3v2bDekXsnd7rjjDpfMLpSCZg1LVzZ4ZbRXg0DPnj1t27ZtLmndkiVL3FByLTl38803p2sY8Nx11102ZMgQF8AqSZ+C+G7durms+xqq79GoAg1LV+K8Tz75xB577DG755573DB/ZbBX0jwl5VPgrNf95ptv7LnnngsmpDtW2ZSx/uGHH7a+ffu6RH96XGV55ZVXcnwdp02b5hpM9FeNJWvWrHE9+iq/GlNE56Dh+l27dnWJA5V477///a/bVx566CE3GkHZ9rVNifg09F/J+I5GyQuVtFBLGuq1V65caRMnTrRnnnkmx+cDAEBO0LMPAECM6N+/v+sVVlZ3zZNXNn4tfadl5DwKrjW3Xr3w6nFfv369awBQ0K6guW3btm4OfI0aNax9+/bBufdptWnTxiZMmODyCGjJvwoVKrggXz3sGv7u0Vx6NUIoc72Oq6B94MCBwcfVYKAGAgW4KrvWtFdv/r/+9S/3uNdDfrSyKVu9ev2VxV5z5TW8X1MKckrXRtfvgQcecLkFNPpA56B8BFrdQHSOms+voF699xpmr+X1vHn69913n7vuOoZyA+iYWq5QxzkajU7Qa2tKho6tYf6akqHpGAAA5KcEZenL11cEAABx4aabbnIJ9D744INoFwUAAGQTw/gBAAAAAPAZgn0AAAAAAHyGYfwAAAAAAPgMPfsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAABg/vL/ATHCNGcQOJ1PAAAAAElFTkSuQmCC", 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", 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", 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", 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" ] @@ -2194,34 +2072,34 @@ "Segment-level statistics for hate speech scores:\n", "\n", "Controversial:\n", - " Count: 2731\n", - " Mean: 0.0113\n", + " Count: 52185\n", + " Mean: 0.0024\n", " Median: 0.0000\n", - " Std Dev: 0.0376\n", + " Std Dev: 0.0174\n", " Min: 0.0000\n", - " Max: 0.2104\n", + " Max: 0.3151\n", " 25th Percentile: 0.0000\n", " 75th Percentile: 0.0000\n", " 90th Percentile: 0.0000\n", - " 95th Percentile: 0.1193\n", - " 99th Percentile: 0.1689\n", + " 95th Percentile: 0.0000\n", + " 99th Percentile: 0.1166\n", " High risk segments (> 0.5): 0 (0.00%)\n", - " Medium risk segments (0.1-0.5): 236 (8.64%)\n", + " Medium risk segments (0.1-0.5): 976 (1.87%)\n", "\n", "Lex Fridman:\n", - " Count: 7153\n", - " Mean: 0.0012\n", + " Count: 7008\n", + " Mean: 0.0013\n", " Median: 0.0000\n", - " Std Dev: 0.0126\n", + " Std Dev: 0.0134\n", " Min: 0.0000\n", - " Max: 0.2409\n", + " Max: 0.2053\n", " 25th Percentile: 0.0000\n", " 75th Percentile: 0.0000\n", " 90th Percentile: 0.0000\n", " 95th Percentile: 0.0000\n", - " 99th Percentile: 0.0000\n", + " 99th Percentile: 0.1017\n", " High risk segments (> 0.5): 0 (0.00%)\n", - " Medium risk segments (0.1-0.5): 67 (0.94%)\n" + " Medium risk segments (0.1-0.5): 72 (1.03%)\n" ] } ], @@ -2344,6 +2222,544 @@ " print(f\" Medium risk segments (0.1-0.5): {medium_risk} ({medium_risk_pct:.2f}%)\")\n" ] }, + { + "cell_type": "markdown", + "id": "fb98a1fd", + "metadata": {}, + "source": [ + "## Tuning aggregation strategies with a simulation\n", + "\n", + "So far every episode was scored with the default aggregator (`skewness`). But Sentinel ships several \"summarize metrics\", and which one works best depends on your data. This section runs a small **simulation** (using `sentinel.simulation`) to compare them systematically on our labeled episodes.\n", + "\n", + "Two very different \"metrics\" are involved, and it helps to keep them separate:\n", + "\n", + "- **Summarize metric (the aggregation):** how an episode's many per-segment scores become a single number. Sentinel offers `skewness`, `mean_of_positives`, `top_k_mean`, `percentile_score`, `softmax_weighted_mean`, and `max_score`. This is *not* a ranking.\n", + "- **Evaluation metric:** given one number per episode plus a known label (controversial = 1, Lex Fridman = 0), how well are the two classes separated? Ranking is only one option. The harness reports **three families** so you can tune for whatever matters to you:\n", + " - **Ranking** (`roc_auc`, `recall_at_n`, `rank_ratio`): where do the known-positive episodes land in the leaderboard?\n", + " - **Threshold / classification** (`precision`, `recall`, `f1`): pick a cutoff on the per-episode score and count hits and misses.\n", + " - **Separation / distribution** (`mean_separation`, `cohens_d`, `ks_statistic`): threshold-free distance between the two score distributions.\n", + "\n", + "The expensive part (running the sentence model) is done once by `score_groups`; comparing aggregators and thresholds afterward is cheap." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a9f35cf0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Built 60 labeled groups: 30 controversial (label=1), 30 Lex Fridman (label=0)\n" + ] + }, + { + "data": { + "text/html": [ + "
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aggregatorroc_aucrecall_at_nprecision_at_nrank_ratiof1precisionrecallmean_separationcohens_dks_statistic
0skewness0.8944440.9000000.9000000.4409450.9180330.9032260.9333330.0835021.0675830.833333
1max_score0.8600000.7666670.7666670.4769980.8484850.7777780.9333330.1041751.7927460.666667
2percentile_score0.8522220.7666670.7666670.4853900.8484850.7777780.9333330.0936931.8039570.666667
3top_k_mean0.8477780.7666670.7666670.4902280.8484850.7777780.9333330.0940761.7568840.666667
4softmax_weighted_mean0.7966670.7666670.7666670.5482230.8484850.7777780.9333330.0792491.7156740.666667
5mean_of_positives0.7955560.7666670.7666670.5495340.8484850.7777780.9333330.0789981.7140800.666667
\n", + "
" + ], + "text/plain": [ + " aggregator roc_auc recall_at_n precision_at_n rank_ratio \\\n", + "0 skewness 0.894444 0.900000 0.900000 0.440945 \n", + "1 max_score 0.860000 0.766667 0.766667 0.476998 \n", + "2 percentile_score 0.852222 0.766667 0.766667 0.485390 \n", + "3 top_k_mean 0.847778 0.766667 0.766667 0.490228 \n", + "4 softmax_weighted_mean 0.796667 0.766667 0.766667 0.548223 \n", + "5 mean_of_positives 0.795556 0.766667 0.766667 0.549534 \n", + "\n", + " f1 precision recall mean_separation cohens_d ks_statistic \n", + "0 0.918033 0.903226 0.933333 0.083502 1.067583 0.833333 \n", + "1 0.848485 0.777778 0.933333 0.104175 1.792746 0.666667 \n", + "2 0.848485 0.777778 0.933333 0.093693 1.803957 0.666667 \n", + "3 0.848485 0.777778 0.933333 0.094076 1.756884 0.666667 \n", + "4 0.848485 0.777778 0.933333 0.079249 1.715674 0.666667 \n", + "5 0.848485 0.777778 0.933333 0.078998 1.714080 0.666667 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "\n", + "import pandas as pd\n", + "\n", + "from sentinel.simulation import (\n", + " LabeledGroup,\n", + " score_groups,\n", + " compare_aggregators,\n", + " run_grid_search,\n", + ")\n", + "\n", + "# Build \"labeled groups\": each podcast episode is one group of text segments.\n", + "# label = 1 -> controversial (rare / positive class)\n", + "# label = 0 -> Lex Fridman (common / negative class)\n", + "controversial_groups = [\n", + " LabeledGroup(name=str(file_path), label=1, observations=group[\"segment\"].tolist())\n", + " for file_path, group in results_df.groupby(\"file\")\n", + "]\n", + "lex_groups = [\n", + " LabeledGroup(name=str(file_path), label=0, observations=group[\"segment\"].tolist())\n", + " for file_path, group in lex_results_df.groupby(\"file\")\n", + "]\n", + "\n", + "# Why 30? It matches the 30 Lex Fridman episodes sampled earlier, so the\n", + "# evaluation set is a balanced 30-vs-30 rather than 100-vs-30. Balance changes\n", + "# what the metrics mean: top_n defaults to the number of positives, and\n", + "# precision/f1 move with the class ratio (roc_auc and recall do not).\n", + "# It also caps the runtime, which scales with the number of *segments* rather\n", + "# than episodes. Raise both sides together to keep the comparison balanced.\n", + "random.seed(0)\n", + "controversial_sample = random.sample(\n", + " controversial_groups, min(30, len(controversial_groups))\n", + ")\n", + "groups = controversial_sample + lex_groups\n", + "\n", + "n_pos = sum(g.label == 1 for g in groups)\n", + "n_neg = sum(g.label == 0 for g in groups)\n", + "print(f\"Built {len(groups)} labeled groups: {n_pos} controversial (label=1), {n_neg} Lex Fridman (label=0)\")\n", + "\n", + "# EXPENSIVE STEP (runs the sentence model): score every segment once. The result\n", + "# is reused by every aggregator below, so we don't re-encode anything.\n", + "scored = score_groups(index, groups, top_k=5, show_progress_bar=True)\n", + "\n", + "# CHEAP STEP: compare all six summarize metrics on the same scores.\n", + "comparison = pd.DataFrame(compare_aggregators(scored))\n", + "comparison_view = (\n", + " comparison[\n", + " [\n", + " \"aggregator\",\n", + " \"roc_auc\",\n", + " \"recall_at_n\",\n", + " \"precision_at_n\",\n", + " \"rank_ratio\",\n", + " \"f1\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"mean_separation\",\n", + " \"cohens_d\",\n", + " \"ks_statistic\",\n", + " ]\n", + " ]\n", + " .sort_values(\"roc_auc\", ascending=False)\n", + " .reset_index(drop=True)\n", + ")\n", + "comparison_view" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "da17416d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Show one representative metric from each evaluation family, per aggregator.\n", + "# We use separate subplots because the families live on different scales\n", + "# (roc_auc and f1 are in [0, 1], while Cohen's d can exceed 1).\n", + "families = [\"roc_auc\", \"f1\", \"cohens_d\"]\n", + "family_titles = {\n", + " \"roc_auc\": \"Ranking (ROC-AUC)\",\n", + " \"f1\": \"Threshold (best F1)\",\n", + " \"cohens_d\": \"Separation (Cohen's d)\",\n", + "}\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "for ax, family in zip(axes, families):\n", + " values = comparison.set_index(\"aggregator\")[family]\n", + " values.plot(kind=\"bar\", ax=ax, color=\"steelblue\", alpha=0.85, edgecolor=\"white\")\n", + " ax.set_title(family_titles[family])\n", + " ax.set_xlabel(\"\")\n", + " ax.tick_params(axis=\"x\", rotation=30)\n", + " ax.grid(True, axis=\"y\", alpha=0.3)\n", + " ax.set_axisbelow(True)\n", + "\n", + "fig.suptitle(\"Aggregator comparison across the three evaluation families\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b196a11d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top configurations by ROC-AUC:\n" + ] + }, + { + "data": { + "text/html": [ + "
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aggregatortop_kmin_score_to_considerroc_aucf1cohens_d
0skewness50.00.9766670.9354842.755040
1skewness30.00.9700000.9333332.669152
2skewness100.00.9533330.9152542.471844
3top_k_mean30.00.9044440.8852461.833128
4skewness50.10.8944440.9180331.067583
5max_score30.00.8911110.8571431.714937
6top_k_mean50.00.8900000.8787881.714391
7skewness30.10.8866670.9000001.170104
8max_score30.10.8800000.8571431.721444
9top_k_mean30.10.8688890.8484851.631476
\n", + "
" + ], + "text/plain": [ + " aggregator top_k min_score_to_consider roc_auc f1 cohens_d\n", + "0 skewness 5 0.0 0.976667 0.935484 2.755040\n", + "1 skewness 3 0.0 0.970000 0.933333 2.669152\n", + "2 skewness 10 0.0 0.953333 0.915254 2.471844\n", + "3 top_k_mean 3 0.0 0.904444 0.885246 1.833128\n", + "4 skewness 5 0.1 0.894444 0.918033 1.067583\n", + "5 max_score 3 0.0 0.891111 0.857143 1.714937\n", + "6 top_k_mean 5 0.0 0.890000 0.878788 1.714391\n", + "7 skewness 3 0.1 0.886667 0.900000 1.170104\n", + "8 max_score 3 0.1 0.880000 0.857143 1.721444\n", + "9 top_k_mean 3 0.1 0.868889 0.848485 1.631476" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Grid search: sweep top_k (each value re-scores the segments) and the\n", + "# per-observation threshold min_score_to_consider (applied cheaply), for every\n", + "# aggregator. Returns one row per (top_k, min_score, aggregator) combination.\n", + "grid = pd.DataFrame(\n", + " run_grid_search(\n", + " index,\n", + " groups,\n", + " top_k_values=[3, 5, 10],\n", + " min_score_values=[0.0, 0.1, 0.25],\n", + " )\n", + ")\n", + "\n", + "# Which single configuration separates the two classes best (by ROC-AUC)?\n", + "best = grid.sort_values(\"roc_auc\", ascending=False).reset_index(drop=True)\n", + "print(\"Top configurations by ROC-AUC:\")\n", + "best[[\"aggregator\", \"top_k\", \"min_score_to_consider\", \"roc_auc\", \"f1\", \"cohens_d\"]].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "20e57208", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap of ROC-AUC over (top_k x min_score_to_consider) for the best aggregator,\n", + "# so we can pick a robust configuration rather than a single lucky point.\n", + "best_aggregator = best.iloc[0][\"aggregator\"]\n", + "sub = grid[grid[\"aggregator\"] == best_aggregator]\n", + "pivot = sub.pivot(index=\"top_k\", columns=\"min_score_to_consider\", values=\"roc_auc\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "im = ax.imshow(pivot.values, cmap=\"viridis\", aspect=\"auto\")\n", + "ax.set_xticks(range(len(pivot.columns)))\n", + "ax.set_xticklabels(pivot.columns)\n", + "ax.set_yticks(range(len(pivot.index)))\n", + "ax.set_yticklabels(pivot.index)\n", + "ax.set_xlabel(\"min_score_to_consider\")\n", + "ax.set_ylabel(\"top_k\")\n", + "ax.set_title(f\"ROC-AUC heatmap for '{best_aggregator}'\")\n", + "for i in range(len(pivot.index)):\n", + " for j in range(len(pivot.columns)):\n", + " ax.text(j, i, f\"{pivot.values[i, j]:.3f}\", ha=\"center\", va=\"center\", color=\"white\")\n", + "fig.colorbar(im, ax=ax, label=\"ROC-AUC\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8054d065", + "metadata": {}, + "source": [ + "### Reading the results\n", + "\n", + "- The **aggregator comparison** shows how the choice of summarize metric changes how cleanly controversial and Lex Fridman episodes separate. If `skewness` is not the top row, another aggregator may fit this dataset better.\n", + "- The three families can disagree: an aggregator can rank well (high `roc_auc`) yet have a modest best-`f1`, or vice versa. Choose the family that matches how you will use the score - triage/ranking (ranking family), a hard yes/no cutoff (threshold family), or distribution monitoring (separation family).\n", + "- The **grid search** reports the best `(top_k, min_score_to_consider)` per aggregator, and the heatmap helps you pick a setting that is good across neighboring values (robust) rather than a single lucky cell.\n", + "\n", + "This ties directly into the \"wolf in sheep's clothing\" appendix below: aggregators such as `skewness`, `top_k_mean`, and `percentile_score` are built to catch a few high-scoring segments hidden inside otherwise benign content - exactly the signal that simple averaging would wash out." + ] + }, + { + "cell_type": "markdown", + "id": "c7c28001", + "metadata": {}, + "source": [ + "### What we found\n", + "\n", + "Three conclusions held up on this dataset, and one trap is worth avoiding.\n", + "\n", + "**Skewness is the right default.** After the grid search, the highest-scoring configurations are all `skewness`, and tuning lifts it clearly above what any aggregator reaches at the out-of-the-box settings. Sentinel's default choice is a good one for content shaped like this - long, mostly benign transcripts with occasional concentrated spikes.\n", + "\n", + "**The noise floor deserves a sweep, not an assumption.** `min_score_to_consider` affects `skewness` far more than the other aggregators, and there is a concrete reason. `skewness` computes `(mean - median) / std` over the *entire* array of segment scores, zeros included, while the other five first filter with `scores[scores > 0]`. Raising the floor therefore reshapes the distribution that `skewness` sees, whereas for the others it simply removes inputs. Its best value also interacts with `top_k`, so sweep the two together rather than tuning either on its own.\n", + "\n", + "**Do not over-read small gaps.** With 30 episodes per class, differences of a few thousandths of ROC-AUC sit inside the margin of error: re-running against a different sample can reorder the leading aggregators. Read the table as \"this group is clearly ahead of that group\" rather than as an exact ranking, and prefer a configuration surrounded by other strong cells in the heatmap over the single best cell, which may just be lucky. Raising the episode counts is the way to tighten this up." + ] + }, { "cell_type": "markdown", "id": "9c91aa37", @@ -2405,9 +2821,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python (Sentinel)", "language": "python", - "name": "python3" + "name": "sentinel" }, "language_info": { "codemirror_mode": { @@ -2419,7 +2835,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.15" + "version": "3.10.20" } }, "nbformat": 4, diff --git a/src/sentinel/__init__.py b/src/sentinel/__init__.py index dca2c35..4b937ba 100644 --- a/src/sentinel/__init__.py +++ b/src/sentinel/__init__.py @@ -28,6 +28,15 @@ softmax_weighted_mean, max_score, ) +from sentinel.simulation import ( + DEFAULT_AGGREGATORS, + LabeledGroup, + GroupObservationScores, + score_groups, + evaluate_groups, + compare_aggregators, + run_grid_search, +) __all__ = [ "SentinelLocalIndex", @@ -38,4 +47,11 @@ "percentile_score", "softmax_weighted_mean", "max_score", + "DEFAULT_AGGREGATORS", + "LabeledGroup", + "GroupObservationScores", + "score_groups", + "evaluate_groups", + "compare_aggregators", + "run_grid_search", ] diff --git a/src/sentinel/simulation.py b/src/sentinel/simulation.py new file mode 100644 index 0000000..03d9f68 --- /dev/null +++ b/src/sentinel/simulation.py @@ -0,0 +1,572 @@ +# Copyright 2025 Roblox Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Simulation and tuning harness for Sentinel. + +This module helps you answer a practical question: *which aggregation +("summarize metric") and which hyperparameters work best for my data?* + +It mirrors the workflow used to evaluate Sentinel in production, but with none +of the production infrastructure (no Ray, S3, Hydra, or experiment trackers) so +it stays easy to read and depends only on numpy. + +There are two very different "metrics" involved, and it helps to keep them +separate: + +1. The **summarize metric** (a.k.a. the aggregation function): how a group's + many per-observation scores are collapsed into a single affinity number. + Sentinel ships several of these in :mod:`sentinel.score_formulae` + (``skewness``, ``mean_of_positives``, ``top_k_mean``, ``percentile_score``, + ``softmax_weighted_mean``, ``max_score``). This is *not* a ranking. + +2. The **evaluation metric**: once every group has one affinity number and a + known label, how good is that separation? Ranking (where do the known + positives land in the leaderboard?) is only one option. This harness reports + three families so you can tune for whatever you care about: + + - ranking (ROC-AUC, recall@N / precision@N, average-rank ratio), + - threshold / classification (precision, recall, F1 at a cutoff), + - separation / distribution (mean separation, Cohen's d, KS statistic). + +Typical usage:: + + from sentinel.simulation import LabeledGroup, score_groups, compare_aggregators + + groups = [ + LabeledGroup(name="episode_a", label=1, observations=[...segments...]), + LabeledGroup(name="episode_b", label=0, observations=[...segments...]), + ... + ] + + # Expensive step (runs the sentence model) -- done once. + scored = score_groups(index, groups, top_k=5) + + # Cheap step -- compare every aggregator without re-encoding. + rows = compare_aggregators(scored) +""" + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Callable, Dict, List, Mapping, Optional, Sequence + +import numpy as np + +from sentinel.score_formulae import ( + max_score, + mean_of_positives, + percentile_score, + skewness, + softmax_weighted_mean, + top_k_mean, +) + +if TYPE_CHECKING: + # Imported only for type hints. Kept out of runtime imports so this module + # stays lightweight (numpy-only) and does not pull in torch / transformers. + from sentinel.sentinel_local_index import SentinelLocalIndex + + +# A convenient name -> function map of the built-in summarize metrics, so a +# caller (or a notebook) can iterate over "all aggregators" in one line. +DEFAULT_AGGREGATORS: Dict[str, Callable[[np.ndarray], float]] = { + "skewness": skewness, + "mean_of_positives": mean_of_positives, + "top_k_mean": top_k_mean, + "percentile_score": percentile_score, + "softmax_weighted_mean": softmax_weighted_mean, + "max_score": max_score, +} + + +@dataclass +class LabeledGroup: + """A collection of observations from a single source, with a known label. + + Think of one group as, for example, one podcast episode (its observations + are the episode's text segments) or one user (their recent messages). + + Attributes: + name: A human-readable identifier for the group. + label: ``1`` if this group belongs to the rare/positive class (e.g. a + hateful episode), ``0`` if it belongs to the common/negative class. + observations: The individual texts that make up this group. + """ + + name: str + label: int + observations: Sequence[str] + + +@dataclass +class GroupObservationScores: + """Raw per-observation scores for one group (computed once, reused often). + + Attributes: + name: The group's identifier (copied from :class:`LabeledGroup`). + label: The group's label (copied from :class:`LabeledGroup`). + observation_scores: 1-D array of per-observation scores, *before* any + ``min_score_to_consider`` threshold is applied. Keeping them raw lets + us sweep thresholds later without re-running the sentence model. + """ + + name: str + label: int + observation_scores: np.ndarray + + +def score_groups( + index: "SentinelLocalIndex", + groups: Sequence[LabeledGroup], + *, + top_k: int = 5, + show_progress_bar: bool = False, + encoding_additional_kwargs: Optional[Mapping[str, object]] = None, +) -> List[GroupObservationScores]: + """Score every observation in every group, once. + + This is the expensive part of a simulation because it runs the sentence + model to embed and score each observation. Aggregation and evaluation happen + separately and cheaply, so you can compare many summarize metrics and + thresholds afterward without paying the encoding cost again. + + Scores are computed with ``min_score_to_consider=0.0`` so the raw values are + preserved; :func:`evaluate_groups` applies any threshold later. + + Args: + index: A loaded :class:`~sentinel.sentinel_local_index.SentinelLocalIndex`. + groups: The labeled groups to score. + top_k: Number of nearest neighbors used per observation. Changing this + changes the per-observation scores, so it requires re-scoring. + show_progress_bar: Whether to show the encoder progress bar. + encoding_additional_kwargs: Extra keyword arguments forwarded to the + encoder. + + Returns: + One :class:`GroupObservationScores` per input group, in the same order. + """ + if encoding_additional_kwargs is None: + encoding_additional_kwargs = {} + + scored: List[GroupObservationScores] = [] + for group in groups: + observations = list(group.observations) + + if len(observations) == 0: + scored.append( + GroupObservationScores( + name=group.name, + label=int(group.label), + observation_scores=np.array([], dtype=float), + ) + ) + continue + + # We only need the per-observation scores here, so we disable the + # explainability extras for speed and pass a trivial aggregation + # function (its output is ignored). + result = index.calculate_rare_class_affinity( + observations, + top_k=top_k, + min_score_to_consider=0.0, + aggregation_function=max_score, + show_progress_bar=show_progress_bar, + explain=False, + include_neighbors=False, + encoding_additional_kwargs=encoding_additional_kwargs, + ) + + observation_scores = np.asarray( + list(result.observation_scores.values()), dtype=float + ) + scored.append( + GroupObservationScores( + name=group.name, + label=int(group.label), + observation_scores=observation_scores, + ) + ) + + return scored + + +def _apply_threshold(scores: np.ndarray, min_score_to_consider: float) -> np.ndarray: + """Zero out per-observation scores below the threshold (cheap, no encoding). + + This reproduces exactly what ``calculate_rare_class_affinity`` does with its + ``min_score_to_consider`` argument, but applied to already-computed scores. + """ + if scores.size == 0 or min_score_to_consider <= 0.0: + return scores + return np.where(scores < min_score_to_consider, 0.0, scores) + + +def _average_ranks(values: np.ndarray) -> np.ndarray: + """Return 1-based ranks of ``values`` (smallest = rank 1), averaging ties. + + Ties receive the average of the ranks they span (the standard convention + used by the Mann-Whitney statistic), which keeps metrics stable when many + groups share the same score (e.g. lots of zeros). + """ + n = values.size + order = np.argsort(values, kind="mergesort") + sorted_vals = values[order] + ranks = np.empty(n, dtype=float) + + i = 0 + while i < n: + j = i + while j + 1 < n and sorted_vals[j + 1] == sorted_vals[i]: + j += 1 + # Average 1-based rank for the tied block [i, j]. + avg_rank = (i + j) / 2.0 + 1.0 + ranks[order[i : j + 1]] = avg_rank + i = j + 1 + + return ranks + + +def _roc_auc(affinities: np.ndarray, labels: np.ndarray) -> float: + """ROC-AUC via the rank-based (Mann-Whitney) formula. numpy-only. + + Equals the probability that a randomly chosen positive group scores higher + than a randomly chosen negative one. Ties count as 0.5. Returns ``nan`` if + either class is missing. + """ + n_pos = int(np.sum(labels == 1)) + n_neg = int(np.sum(labels == 0)) + if n_pos == 0 or n_neg == 0: + return float("nan") + + ranks = _average_ranks(affinities) + sum_ranks_pos = float(np.sum(ranks[labels == 1])) + return (sum_ranks_pos - n_pos * (n_pos + 1) / 2.0) / (n_pos * n_neg) + + +def _ranking_metrics( + affinities: np.ndarray, labels: np.ndarray, top_n: Optional[int] +) -> Dict[str, float]: + """Ranking family: leaderboard-style metrics (production's original choice).""" + n_groups = int(labels.size) + n_pos = int(np.sum(labels == 1)) + n_neg = int(np.sum(labels == 0)) + + metrics: Dict[str, float] = {"roc_auc": _roc_auc(affinities, labels)} + + # Leaderboard rank where 1 = highest affinity. Rank the *negated* affinities + # so the largest affinity gets the smallest rank number. Ties are averaged. + leaderboard_rank = _average_ranks(-affinities) + + # recall@N / precision@N over the top-N of the leaderboard. Default N is the + # number of positives (a.k.a. R-precision), which mirrors production intent. + top_n_eff = n_pos if top_n is None else int(top_n) + top_n_eff = max(0, min(top_n_eff, n_groups)) + metrics["top_n"] = float(top_n_eff) + if top_n_eff > 0 and n_pos > 0: + in_top = labels[leaderboard_rank <= top_n_eff] + tp_in_top = int(np.sum(in_top == 1)) + metrics["precision_at_n"] = tp_in_top / top_n_eff + metrics["recall_at_n"] = tp_in_top / n_pos + else: + metrics["precision_at_n"] = float("nan") + metrics["recall_at_n"] = float("nan") + + metrics["avg_rank_positive"] = ( + float(np.mean(leaderboard_rank[labels == 1])) if n_pos > 0 else float("nan") + ) + metrics["avg_rank_negative"] = ( + float(np.mean(leaderboard_rank[labels == 0])) if n_neg > 0 else float("nan") + ) + # Lower is better: positives should have smaller (better) ranks than negatives. + if n_pos > 0 and n_neg > 0 and metrics["avg_rank_negative"] > 0: + metrics["rank_ratio"] = ( + metrics["avg_rank_positive"] / metrics["avg_rank_negative"] + ) + else: + metrics["rank_ratio"] = float("nan") + + return metrics + + +def _classification_at_threshold( + affinities: np.ndarray, labels: np.ndarray, threshold: float +) -> Dict[str, float]: + """Confusion-matrix counts and precision/recall/F1 at a fixed cutoff.""" + predicted_positive = affinities >= threshold + tp = int(np.sum(predicted_positive & (labels == 1))) + fp = int(np.sum(predicted_positive & (labels == 0))) + fn = int(np.sum(~predicted_positive & (labels == 1))) + tn = int(np.sum(~predicted_positive & (labels == 0))) + + precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0 + recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0 + f1 = ( + 2 * precision * recall / (precision + recall) + if (precision + recall) > 0 + else 0.0 + ) + false_positive_rate = fp / (fp + tn) if (fp + tn) > 0 else 0.0 + + return { + "tp": float(tp), + "fp": float(fp), + "fn": float(fn), + "tn": float(tn), + "precision": precision, + "recall": recall, + "f1": f1, + "false_positive_rate": false_positive_rate, + } + + +def _threshold_metrics( + affinities: np.ndarray, labels: np.ndarray, decision_threshold: Optional[float] +) -> Dict[str, float]: + """Threshold / classification family. + + If ``decision_threshold`` is given, metrics are reported at that exact + cutoff. If it is ``None``, we sweep the candidate cutoffs (the sorted unique + affinities) and report the one that maximizes F1 -- a useful "best + achievable" signal when tuning. + """ + n_pos = int(np.sum(labels == 1)) + n_neg = int(np.sum(labels == 0)) + if n_pos == 0 or n_neg == 0: + return { + "decision_threshold": float("nan"), + "tp": float("nan"), + "fp": float("nan"), + "fn": float("nan"), + "tn": float("nan"), + "precision": float("nan"), + "recall": float("nan"), + "f1": float("nan"), + "false_positive_rate": float("nan"), + } + + if decision_threshold is not None: + metrics = _classification_at_threshold( + affinities, labels, float(decision_threshold) + ) + metrics["decision_threshold"] = float(decision_threshold) + return metrics + + best_metrics: Optional[Dict[str, float]] = None + best_threshold = float("nan") + for candidate in np.unique(affinities): + candidate_metrics = _classification_at_threshold(affinities, labels, candidate) + if best_metrics is None or candidate_metrics["f1"] > best_metrics["f1"]: + best_metrics = candidate_metrics + best_threshold = float(candidate) + + assert best_metrics is not None # np.unique is non-empty when groups exist + best_metrics["decision_threshold"] = best_threshold + return best_metrics + + +def _ks_statistic(positives: np.ndarray, negatives: np.ndarray) -> float: + """Two-sample Kolmogorov-Smirnov statistic: max gap between the two CDFs.""" + grid = np.sort(np.concatenate([positives, negatives])) + cdf_pos = np.searchsorted(np.sort(positives), grid, side="right") / positives.size + cdf_neg = np.searchsorted(np.sort(negatives), grid, side="right") / negatives.size + return float(np.max(np.abs(cdf_pos - cdf_neg))) + + +def _separation_metrics( + affinities: np.ndarray, labels: np.ndarray +) -> Dict[str, float]: + """Separation / distribution family (threshold-free, non-ranking).""" + positives = affinities[labels == 1] + negatives = affinities[labels == 0] + + if positives.size == 0 or negatives.size == 0: + return { + "mean_separation": float("nan"), + "cohens_d": float("nan"), + "ks_statistic": float("nan"), + } + + mean_pos = float(np.mean(positives)) + mean_neg = float(np.mean(negatives)) + mean_separation = mean_pos - mean_neg + + # Pooled standard deviation for Cohen's d (standardized effect size). + n1, n2 = positives.size, negatives.size + var_pos = float(np.var(positives, ddof=1)) if n1 > 1 else 0.0 + var_neg = float(np.var(negatives, ddof=1)) if n2 > 1 else 0.0 + if (n1 + n2 - 2) > 0: + pooled_var = ((n1 - 1) * var_pos + (n2 - 1) * var_neg) / (n1 + n2 - 2) + else: + pooled_var = 0.0 + pooled_std = float(np.sqrt(pooled_var)) + + return { + "mean_separation": mean_separation, + "cohens_d": (mean_separation / pooled_std) if pooled_std > 0 else float("nan"), + "ks_statistic": _ks_statistic(positives, negatives), + } + + +def evaluate_groups( + group_scores: Sequence[GroupObservationScores], + aggregator: Callable[[np.ndarray], float], + *, + aggregator_name: Optional[str] = None, + min_score_to_consider: float = 0.1, + top_n: Optional[int] = None, + decision_threshold: Optional[float] = None, +) -> Dict[str, float]: + """Aggregate each group to one affinity, then score it against the labels. + + Produces a single flat dict containing all three metric families (ranking, + threshold/classification, and separation/distribution) plus some metadata, + so you can tune for whichever family matters most to you. + + Args: + group_scores: Output of :func:`score_groups`. + aggregator: A summarize metric such as ``skewness`` or ``top_k_mean``. + aggregator_name: Optional label for the aggregator (defaults to its + ``__name__``). + min_score_to_consider: Per-observation threshold applied before + aggregating (values below it are set to 0). + top_n: Size of the top-N slice for recall@N / precision@N. Defaults to + the number of positive groups. + decision_threshold: Cutoff for the classification family. If ``None``, + the best-F1 cutoff is found automatically. + + Returns: + A dict with keys for metadata (``aggregator``, ``min_score_to_consider``, + ``n_groups``, ``n_positive``) and every metric from the three families. + """ + labels_list: List[int] = [] + affinities_list: List[float] = [] + for group in group_scores: + thresholded = _apply_threshold( + group.observation_scores, min_score_to_consider + ) + affinity = float(aggregator(thresholded)) if thresholded.size > 0 else 0.0 + labels_list.append(int(group.label)) + affinities_list.append(affinity) + + labels = np.asarray(labels_list, dtype=int) + affinities = np.asarray(affinities_list, dtype=float) + + row: Dict[str, float] = { + "aggregator": aggregator_name + or getattr(aggregator, "__name__", str(aggregator)), + "min_score_to_consider": float(min_score_to_consider), + "n_groups": int(labels.size), + "n_positive": int(np.sum(labels == 1)), + } + row.update(_ranking_metrics(affinities, labels, top_n=top_n)) + row.update(_threshold_metrics(affinities, labels, decision_threshold=decision_threshold)) + row.update(_separation_metrics(affinities, labels)) + return row + + +def compare_aggregators( + group_scores: Sequence[GroupObservationScores], + *, + aggregators: Optional[Mapping[str, Callable[[np.ndarray], float]]] = None, + min_score_to_consider: float = 0.1, + top_n: Optional[int] = None, + decision_threshold: Optional[float] = None, +) -> List[Dict[str, float]]: + """Evaluate several summarize metrics on the same pre-computed scores. + + This is cheap: it reuses the per-observation scores from + :func:`score_groups`, so no re-encoding happens. + + Args: + group_scores: Output of :func:`score_groups`. + aggregators: Name -> function map. Defaults to + :data:`DEFAULT_AGGREGATORS` (all six built-ins). + min_score_to_consider: Per-observation threshold applied before + aggregating. + top_n: Size of the top-N slice for recall@N / precision@N. + decision_threshold: Cutoff for the classification family (``None`` = + best-F1). + + Returns: + One metric dict per aggregator (see :func:`evaluate_groups`). + """ + if aggregators is None: + aggregators = DEFAULT_AGGREGATORS + + return [ + evaluate_groups( + group_scores, + fn, + aggregator_name=name, + min_score_to_consider=min_score_to_consider, + top_n=top_n, + decision_threshold=decision_threshold, + ) + for name, fn in aggregators.items() + ] + + +def run_grid_search( + index: "SentinelLocalIndex", + groups: Sequence[LabeledGroup], + *, + top_k_values: Sequence[int] = (5,), + min_score_values: Sequence[float] = (0.1,), + aggregators: Optional[Mapping[str, Callable[[np.ndarray], float]]] = None, + top_n: Optional[int] = None, + decision_threshold: Optional[float] = None, + show_progress_bar: bool = False, +) -> List[Dict[str, float]]: + """Sweep hyperparameters and summarize metrics, returning a flat result table. + + For each ``top_k`` the groups are scored once (the expensive step), then + every combination of ``min_score_values`` x ``aggregators`` is evaluated + cheaply. The returned rows are easy to turn into a ``pandas.DataFrame`` for + plotting. + + Args: + index: A loaded :class:`~sentinel.sentinel_local_index.SentinelLocalIndex`. + groups: The labeled groups to evaluate. + top_k_values: ``top_k`` values to try (each triggers a re-scoring). + min_score_values: Per-observation thresholds to try (evaluated cheaply). + aggregators: Name -> function map. Defaults to :data:`DEFAULT_AGGREGATORS`. + top_n: Size of the top-N slice for recall@N / precision@N. + decision_threshold: Cutoff for the classification family (``None`` = + best-F1). + show_progress_bar: Whether to show the encoder progress bar. + + Returns: + A list of metric dicts, one per ``(top_k, min_score_to_consider, + aggregator)`` combination. Each row also includes a ``top_k`` key. + """ + if aggregators is None: + aggregators = DEFAULT_AGGREGATORS + + rows: List[Dict[str, float]] = [] + for top_k in top_k_values: + scored = score_groups( + index, groups, top_k=top_k, show_progress_bar=show_progress_bar + ) + for min_score in min_score_values: + for name, fn in aggregators.items(): + row = evaluate_groups( + scored, + fn, + aggregator_name=name, + min_score_to_consider=min_score, + top_n=top_n, + decision_threshold=decision_threshold, + ) + row["top_k"] = int(top_k) + rows.append(row) + + return rows diff --git a/tests/test_sriracha_local_index.py b/tests/test_sentinel_local_index.py similarity index 100% rename from tests/test_sriracha_local_index.py rename to tests/test_sentinel_local_index.py diff --git a/tests/test_simulation.py b/tests/test_simulation.py new file mode 100644 index 0000000..2275288 --- /dev/null +++ b/tests/test_simulation.py @@ -0,0 +1,261 @@ +# Copyright 2025 Roblox Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for the simulation / tuning harness (sentinel.simulation). + +These tests are fast and require no model download: they either exercise the +pure metric logic directly on hand-crafted scores, or use a tiny stub index +that mimics ``calculate_rare_class_affinity``. +""" + +from types import SimpleNamespace + +import numpy as np +import pytest + +from sentinel.score_formulae import max_score +from sentinel.simulation import ( + DEFAULT_AGGREGATORS, + GroupObservationScores, + LabeledGroup, + compare_aggregators, + evaluate_groups, + run_grid_search, + score_groups, +) + + +def _make_group_scores(pairs): + """Build GroupObservationScores from (label, affinity) pairs. + + Each group gets a single observation equal to the desired affinity, so that + aggregating with ``max_score`` (and no threshold) reproduces that affinity. + """ + return [ + GroupObservationScores( + name=f"g{i}", label=label, observation_scores=np.array([affinity], dtype=float) + ) + for i, (label, affinity) in enumerate(pairs) + ] + + +# --------------------------------------------------------------------------- +# Ranking family +# --------------------------------------------------------------------------- + + +def test_roc_auc_separable(): + """Positives clearly above negatives -> AUC 1.0 and perfect top-N.""" + scores = _make_group_scores([(1, 0.9), (1, 0.8), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + + assert row["roc_auc"] == pytest.approx(1.0) + assert row["recall_at_n"] == pytest.approx(1.0) + assert row["precision_at_n"] == pytest.approx(1.0) + # Positives should rank better (smaller rank number) than negatives. + assert row["avg_rank_positive"] < row["avg_rank_negative"] + assert row["rank_ratio"] < 1.0 + assert row["n_groups"] == 4 + assert row["n_positive"] == 2 + assert row["top_n"] == 2 + + +def test_roc_auc_reversed(): + """Positives below negatives -> AUC 0.0.""" + scores = _make_group_scores([(1, 0.1), (1, 0.2), (0, 0.9), (0, 0.8)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert row["roc_auc"] == pytest.approx(0.0) + + +def test_roc_auc_tied(): + """All affinities equal -> AUC 0.5 (ties are averaged).""" + scores = _make_group_scores([(1, 0.5), (1, 0.5), (0, 0.5), (0, 0.5)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert row["roc_auc"] == pytest.approx(0.5) + + +def test_ranking_auc_undefined_single_class(): + """AUC is nan when only one class is present.""" + scores = _make_group_scores([(1, 0.9), (1, 0.8)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert np.isnan(row["roc_auc"]) + + +# --------------------------------------------------------------------------- +# Threshold / classification family +# --------------------------------------------------------------------------- + + +def test_classification_fixed_threshold(): + """A cutoff between the two classes yields perfect precision/recall/F1.""" + scores = _make_group_scores([(1, 0.9), (1, 0.8), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0, decision_threshold=0.5) + + assert row["decision_threshold"] == pytest.approx(0.5) + assert row["precision"] == pytest.approx(1.0) + assert row["recall"] == pytest.approx(1.0) + assert row["f1"] == pytest.approx(1.0) + assert row["false_positive_rate"] == pytest.approx(0.0) + assert (row["tp"], row["fp"], row["fn"], row["tn"]) == (2.0, 0.0, 0.0, 2.0) + + +def test_classification_flag_everything_threshold(): + """A very low cutoff flags all groups -> recall 1.0, precision = base rate.""" + scores = _make_group_scores([(1, 0.9), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0, decision_threshold=0.0) + assert row["recall"] == pytest.approx(1.0) + assert row["precision"] == pytest.approx(1.0 / 3.0) + + +def test_classification_best_f1_sweep(): + """With decision_threshold=None the best-F1 cutoff is found automatically.""" + scores = _make_group_scores([(1, 0.9), (1, 0.8), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + # Perfectly separable -> a threshold achieving F1 == 1.0 exists. + assert row["f1"] == pytest.approx(1.0) + # The best cutoff is the lowest positive value (0.8), since predictions use >=. + assert row["decision_threshold"] == pytest.approx(0.8) + + +# --------------------------------------------------------------------------- +# Separation / distribution family +# --------------------------------------------------------------------------- + + +def test_separation_metrics_basic(): + scores = _make_group_scores([(1, 0.9), (1, 0.7), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert row["mean_separation"] == pytest.approx(0.8 - 0.15) + assert row["cohens_d"] > 0.0 + assert 0.0 <= row["ks_statistic"] <= 1.0 + assert row["ks_statistic"] == pytest.approx(1.0) # fully separated + + +def test_cohens_d_nan_when_zero_variance(): + """Identical values within each class -> pooled std 0 -> Cohen's d is nan.""" + scores = _make_group_scores([(1, 0.5), (1, 0.5), (0, 0.2), (0, 0.2)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert row["mean_separation"] == pytest.approx(0.3) + assert np.isnan(row["cohens_d"]) + + +def test_separation_nan_single_class(): + scores = _make_group_scores([(0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert np.isnan(row["mean_separation"]) + assert np.isnan(row["ks_statistic"]) + + +# --------------------------------------------------------------------------- +# compare_aggregators +# --------------------------------------------------------------------------- + + +def test_compare_aggregators_returns_row_per_aggregator(): + scores = _make_group_scores([(1, 0.9), (1, 0.8), (0, 0.2), (0, 0.1)]) + rows = compare_aggregators(scores, min_score_to_consider=0.0) + + assert len(rows) == len(DEFAULT_AGGREGATORS) + assert {r["aggregator"] for r in rows} == set(DEFAULT_AGGREGATORS.keys()) + + expected_keys = { + # metadata + "aggregator", "min_score_to_consider", "n_groups", "n_positive", + # ranking + "roc_auc", "recall_at_n", "precision_at_n", "top_n", + "avg_rank_positive", "avg_rank_negative", "rank_ratio", + # threshold / classification + "decision_threshold", "tp", "fp", "fn", "tn", + "precision", "recall", "f1", "false_positive_rate", + # separation / distribution + "mean_separation", "cohens_d", "ks_statistic", + } + for row in rows: + assert expected_keys.issubset(row.keys()) + + +# --------------------------------------------------------------------------- +# score_groups / run_grid_search plumbing (with a stub index) +# --------------------------------------------------------------------------- + + +class _StubIndex: + """Minimal stand-in for SentinelLocalIndex. + + Interprets each observation string as its numeric score, so tests can + control per-observation scores exactly with no model. + """ + + def __init__(self): + self.calls = [] + + def calculate_rare_class_affinity(self, text_samples, **kwargs): + self.calls.append(kwargs) + observation_scores = {text: float(text) for text in text_samples} + return SimpleNamespace(observation_scores=observation_scores) + + +def test_score_groups_extracts_raw_scores(): + index = _StubIndex() + groups = [ + LabeledGroup(name="pos", label=1, observations=["0.9", "0.1"]), + LabeledGroup(name="neg", label=0, observations=["0.2", "0.05"]), + ] + + scored = score_groups(index, groups, top_k=7) + + assert [s.name for s in scored] == ["pos", "neg"] + assert [s.label for s in scored] == [1, 0] + np.testing.assert_allclose(scored[0].observation_scores, [0.9, 0.1]) + np.testing.assert_allclose(scored[1].observation_scores, [0.2, 0.05]) + + # score_groups must request raw scores (threshold 0) and disable extras. + call = index.calls[0] + assert call["top_k"] == 7 + assert call["min_score_to_consider"] == 0.0 + assert call["explain"] is False + assert call["include_neighbors"] is False + + +def test_score_groups_handles_empty_group(): + index = _StubIndex() + groups = [LabeledGroup(name="empty", label=1, observations=[])] + scored = score_groups(index, groups) + assert scored[0].observation_scores.size == 0 + # No scoring call should be made for an empty group. + assert index.calls == [] + + +def test_run_grid_search_rescoring_and_rows(): + index = _StubIndex() + groups = [ + LabeledGroup(name="pos", label=1, observations=["0.9", "0.8"]), + LabeledGroup(name="neg", label=0, observations=["0.2", "0.1"]), + ] + + rows = run_grid_search( + index, + groups, + top_k_values=[3, 5], + min_score_values=[0.0, 0.2], + top_n=1, + ) + + # 2 top_k x 2 thresholds x 6 aggregators. + assert len(rows) == 2 * 2 * len(DEFAULT_AGGREGATORS) + assert all("top_k" in row for row in rows) + assert {row["top_k"] for row in rows} == {3, 5} + # Re-scoring happens once per top_k value (each scores both groups), and NOT + # again for every threshold/aggregator combination: 2 top_k x 2 groups = 4. + assert len(index.calls) == 2 * len(groups)