{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
Peter Norvig
2012
Updated 2020
\n", "\n", "# Poker: Ranking Hands, etc.\n", "\n", "\n", "The [rules for poker hands](https://en.wikipedia.org/wiki/List_of_poker_hands) are complex, but it is an interesting exercise to write a program to rank poker hands—to determine if one is higher or lower than another—as I did in my [Udacity 212](https://www.udacity.com/course/design-of-computer-programs--cs212) course after making a [cheesy video](https://www.youtube.com/watch?v=PI8Fo1vzUPM) with David Evans. We'll cover only the ranking part of poker, not the betting part. Some key concepts:\n", "\n", "- **Card**: A card will be represented as a two character string, like `'9c'`, where the first character is the **rank** and the second is the **suit**. The ranks are `23456789TJQKA` in ascending order of value and the suits are `'cdhs'` for clubs, diamonds, hearts, and spades; all suits have equal value in poker. I thought about using the Unicode characters `'♣︎♢♡♠︎'`, but they are hard to find on the keyboard. I also thought of using `(10, 0)` instead of `'Tc'`; the former might allow for a bit faster code, but the later is easier to look at when debugging.\n", "- **Hand**: A hand is a sequence of five cards: `['3s', '3c', 'As', 'Ks', 'Qs']`\n", "- **Type**: A hand has a ranking **type**; these are (in highest to lowest order): five of a kind, straight flush, four of a kind, full house, flush, straight, three of a kind, two pair, one pair, high card. (Five of a kind can only be made in games with wild cards.)\n", "- **Group**: A collection of cards with the same rank: a pair, a three-of-a-kind, etc.\n", "- **Winning**: If two hands have different types, the higher type wins. If they have the same type, a **tiebreaker** is needed. For example, the tiebreaker for \"straight\" is the rank of the highest card; a ten-high straight beats a nine-high straight.\n", "- **Ranking**: To determine who wins, we assign a **ranking** to each hand; the ranking encompasses the type and tiebreaker. The winner is the hand with the highest ranking. Note that the word **ranking** refers to the value of a *hand*, but **rank** refers to the value of a *card*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ranking and Integer Partitions\n", "\n", "There is a curious correspondence between the seven types of poker hands that involve groups and the seven **[integer partitions](https://en.wikipedia.org/wiki/Partition_(number_theory))** of the number 5. \n", "\n", "Consider this table of ranking types:\n", "\n", "\n", "| Type| # | Example          |Partitions |\n", "|-|--|---|---|\n", "| Five of a kind |9|`As Ac Ah Ad Aj`| (5,) |\n", "| Straight flush |8|`As Ks Qs Ts Js`| |\n", "| Four of a kind |7|`7s 7c 7d 2d 7h`| (4, 1)|\n", "| Full house |6| `8h 9c 8d 8c 9h`| (3, 2)|\n", "| Flush |5| `8c Kc Qc Jc Tc`| |\n", "| Straight |4| `Kc Qh Jd Th 9c`| |\n", "| Three of a kind |3| `Ts Tc Th 9s 7c`| (3, 1, 1)|\n", "| Two pair |2| `Ts Tc 9s 9c 7h`| (2, 2, 1) |\n", "| One pair |1| `3s 3c As Ks Qs`| (2, 1, 1, 1)|\n", "| High card |0| `2s 4s 5s 6s 7h`| (1, 1, 1, 1, 1)|\n", "\n", "The types are sorted from highest to lowest, and the partitions are also sorted in lexicographic order from highest to lowest. The correspondence is that, for example, `(3, 2)` means \"three cards of one rank and two cards of another rank\", which is the definition of a full house. \n", "\n", "Let's get some preliminaries out of the way so we can start to program a solution for ranking." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import random\n", "import matplotlib.pyplot as plt\n", "from collections import Counter\n", "from statistics import mean\n", "from itertools import combinations, permutations, product\n", "from functools import lru_cache" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Data Types\n", "Card = str # A card is a str of length 2: '7s'\n", "Hand = (list, tuple) # A hand is 5 cards in either a list or a tuple\n", "\n", "# Functions and Objects\n", "join = ' '.join # Function to join cards together into one string\n", "cards = str.split # Function to split a string apart into a list of cards\n", "rankstr = '23456789TJQKA' # Card ranks in ascending order\n", "\n", "def rank(card) -> int: return rankstr.index(card[0]) + 2\n", "def suit(card) -> str: return card[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `collections.Counter` does most of the work of finding groups and thus partitions. It tells us that the following full house has three 8s and two 9s:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({8: 3, 9: 2})" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hand = cards('8h 9c 8d 8c 9h')\n", "\n", "Counter(map(rank, hand))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This gives us all the information we need; the problem is that it is not in the right format to compare rankings. We can't do `counter1 < counter2` because Counters do not support comparison. But we can reformat the information into a tuple consisting of two components: \n", " 1. The **type** of hand, full house, which is denoted by the partition `(3, 2)`.\n", " 2. The **tiebreakers**: among all full houses, ties go to the highest three-of-a-kind card, and if those are the same, then the highest pair card. So the tiebreaker for this hand would be `(8, 9)`. (Even though 9 > 8, the 8 comes first because the three-of-a-kind is more important than the pair.)\n", "\n", "Thus the complete ranking for this hand is the tuple `((3, 2), (8, 9))`. The same information as in the Counter, but in the proper order for sorting.\n", "\n", "So (ignoring straights and flushes for now) here's how we do `ranking`:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def ranking(hand) -> tuple:\n", " \"\"\"Return a (type, tiebreaker) tuple indicating how high the hand ranks.\"\"\"\n", " counts = Counter(map(rank, hand))\n", " groups = sorted(((counts[r], r) for r in counts), reverse=True)\n", " return tuple(zip(*groups))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the process step by step:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({8: 3, 9: 2})" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts = Counter(map(rank, hand))\n", "counts" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(3, 8), (2, 9)]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "groups = sorted(((counts[r], r) for r in counts), reverse=True)\n", "groups" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((3, 2), (8, 9))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tuple(zip(*groups))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that this final result is the **matrix transpose** (switching rows and columns) of `groups`. In Python the transpose of `m` is `zip(*m)`.\n", "\n", "Below we see that the eights-over-nines full house beats a six-over-tens full house (it doesn't matter that 10 > 9; what matters is 8 > 6):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "hand2 = cards('6h 6c 6d Tc Th')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((3, 2), (8, 9))" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ranking(hand)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((3, 2), (6, 10))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ranking(hand2)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ranking(hand) > ranking(hand2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ranking Straights and Flushes\n", "\n", "It is a cute mathematical result that poker rankings correspond to the integer partitions of 5, but the correspondence does not account for straights and flushes. Still, we can salvage the approach by inventing \"type tuples\" for straight, flush, and straight flush that are not partitions but are in the correct sort order with respect to the seven actual partitions. There are many possible choices; my choices, shown in **bold** below, are to look at the type one row below and add 1 to the last digit: one better than `(3, 1, 1)` is `(3, 1, 2)`; one better than `(4, 1)` is `(4, 2)`.\n", "\n", "\n", "| Type| # | Example          |Type tuple |\n", "|-|--|---|---|\n", "| Five of a kind |9|`As Ac Ah Ad Aj`| (5,) |\n", "| Straight flush |8|`As Ks Qs Ts Js`| **(4, 2)**|\n", "| Four of a kind |7|`7s 7c 7d 2d 7h`| (4, 1)|\n", "| Full house |6| `8h 9c 8d 8c 9h`| (3, 2)|\n", "| Flush |5| `8c Kc Qc Jc Tc`| **(3, 1, 3)** |\n", "| Straight |4| `Kc Qh Jd Th 9c`| **(3, 1, 2)** |\n", "| Three of a kind |3| `Ts Tc Th 9s 7c`| (3, 1, 1)|\n", "| Two pair |2| `Ts Tc 9s 9c 7h`| (2, 2, 1) |\n", "| One pair |1| `3s 3c As Ks Qs`| (2, 1, 1, 1)|\n", "| High card |0| `2s 4s 5s 6s 7h`| (1, 1, 1, 1, 1)|\n", "\n", "It is easy to code this up; the function `special_type` handles flushes and straights, and `rankings` uses the special type or the regular type. Another complication is that in poker aces are not always high: If we have a 5-4-3-2-A hand, the ace serves as a `1` rather than a `14`; we adjust `ranks` accordingly in `rankings`. To test for a flush, we look at the collection of suits and ask if they are all `the_same`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def ranking(hand) -> tuple:\n", " \"\"\"Return a value indicating how high the hand ranks.\"\"\"\n", " counts = Counter(map(rank, hand))\n", " groups = sorted(((counts[r], r) for r in counts), reverse=True)\n", " type, ranks = zip(*groups)\n", " if ranks == (14, 5, 4, 3, 2):\n", " ranks = (5, 4, 3, 2, 1)\n", " return special_type(hand, ranks) or type, ranks\n", "\n", "def special_type(hand, ranks) -> tuple:\n", " \"\"\"For a flush or straight, return a tuple comparable with `type` in `ranking`.\"\"\"\n", " straight = len(ranks) == 5 and max(ranks) - min(ranks) == 4\n", " flush = the_same(map(suit, hand))\n", " return ((4, 2) if straight and flush else \n", " (3, 1, 3) if flush else \n", " (3, 1, 2) if straight else ())\n", "\n", "def the_same(things) -> bool: return len(set(things)) <= 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Testing Poker Hands\n", "\n", "Below is a list of `hands`, with four examples of each **type**, all in descending ranking order (i.e. best to worst). The `test` function asserts that (1) the `ranking` function agrees with this ordering; (2) the order of cards within a hand does not matter—every permutation of cards is ranked the same; and (3) swapping the suits around (e.g. swapping every spade with every club) does not change the ranking." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "hands = [cards(h) for h in ( \n", " 'As Ac Ah Ad Ad', 'Kh Kd Ks Kc Kh', '3h 3s 3d 3c 3c', '2s 2c 2d 2h 2h', # 5 of a kind \n", " 'As Ks Qs Ts Js', 'Kc Qc Jc Tc 9c', '6d 5d 4d 3d 2d', '5h 4h 3h 2h Ah', # straight flush \n", " 'As Ac Ad Ah 2s', '7s 7c 7d 2d 7h', '6s 6c 6d 6h 9s', 'As 5h 5c 5d 5s', # four of a kind \n", " 'Th Tc Td 5h 5c', '9h 9c 9d 8c 8h', '6h 6c 6d Tc Th', '5c 5d 5s As Ah', # full house\n", " 'As 2s 3s 4s 6s', 'Kc Qc Jc Tc 2c', 'Qc Jc Tc 9c 7c', '4h 5h 6h 7h 9h', # flush\n", " 'As Kd Qc Td Jh', 'Kc Qh Jd Th 9c', '6c 5d 4h 3s 2s', 'As 2d 3c 4h 5s', # straight\n", " 'As Ac Ad 2h 3h', 'Ts Tc Th 9s 8c', 'Ts Tc Th 9s 7c', '9h 9s 9d Ah Kh', # three of a kind\n", " 'Ts Tc 5s 5c 8h', 'Ts Tc 5s 5c 7h', '9s 9c 8s 8c As', '3s 3c 2s 2d Ah', # two pair \n", " 'As Ac 4c 5s 6s', '4s 4c As Ks Qs', '4h 4d Kh Qd Jd', '2d 2c Ad Kd Qd', # pair \n", " 'Ah 3s 4s 5s 6s', 'Kh Qh Jh Th 8d', '7d 2s 4s 5s 6s', '7h 6s 5d 3s 2d', # high card\n", " )]\n", "\n", "def test(ranking, hands=hands) -> bool:\n", " \"\"\"Test that `ranking` preserves order of `hands`, and that permuting cards is irrelevant.\"\"\"\n", " assert hands == sorted(hands, key=ranking, reverse=True)\n", " trans = str.maketrans('shdc', 'cdhs')\n", " for hand in hands: \n", " assert the_same(ranking(h) for h in permutations(hand))\n", " assert the_same([ranking(hand), ranking([c.translate(trans) for c in hand])])\n", " return len(hands)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "40" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test(ranking)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what rankings look like:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'As Ac Ah Ad Ad': ((5,), (14,)),\n", " 'Kh Kd Ks Kc Kh': ((5,), (13,)),\n", " '3h 3s 3d 3c 3c': ((5,), (3,)),\n", " '2s 2c 2d 2h 2h': ((5,), (2,)),\n", " 'As Ks Qs Ts Js': ((4, 2), (14, 13, 12, 11, 10)),\n", " 'Kc Qc Jc Tc 9c': ((4, 2), (13, 12, 11, 10, 9)),\n", " '6d 5d 4d 3d 2d': ((4, 2), (6, 5, 4, 3, 2)),\n", " '5h 4h 3h 2h Ah': ((4, 2), (5, 4, 3, 2, 1)),\n", " 'As Ac Ad Ah 2s': ((4, 1), (14, 2)),\n", " '7s 7c 7d 2d 7h': ((4, 1), (7, 2)),\n", " '6s 6c 6d 6h 9s': ((4, 1), (6, 9)),\n", " 'As 5h 5c 5d 5s': ((4, 1), (5, 14)),\n", " 'Th Tc Td 5h 5c': ((3, 2), (10, 5)),\n", " '9h 9c 9d 8c 8h': ((3, 2), (9, 8)),\n", " '6h 6c 6d Tc Th': ((3, 2), (6, 10)),\n", " '5c 5d 5s As Ah': ((3, 2), (5, 14)),\n", " 'As 2s 3s 4s 6s': ((3, 1, 3), (14, 6, 4, 3, 2)),\n", " 'Kc Qc Jc Tc 2c': ((3, 1, 3), (13, 12, 11, 10, 2)),\n", " 'Qc Jc Tc 9c 7c': ((3, 1, 3), (12, 11, 10, 9, 7)),\n", " '4h 5h 6h 7h 9h': ((3, 1, 3), (9, 7, 6, 5, 4)),\n", " 'As Kd Qc Td Jh': ((3, 1, 2), (14, 13, 12, 11, 10)),\n", " 'Kc Qh Jd Th 9c': ((3, 1, 2), (13, 12, 11, 10, 9)),\n", " '6c 5d 4h 3s 2s': ((3, 1, 2), (6, 5, 4, 3, 2)),\n", " 'As 2d 3c 4h 5s': ((3, 1, 2), (5, 4, 3, 2, 1)),\n", " 'As Ac Ad 2h 3h': ((3, 1, 1), (14, 3, 2)),\n", " 'Ts Tc Th 9s 8c': ((3, 1, 1), (10, 9, 8)),\n", " 'Ts Tc Th 9s 7c': ((3, 1, 1), (10, 9, 7)),\n", " '9h 9s 9d Ah Kh': ((3, 1, 1), (9, 14, 13)),\n", " 'Ts Tc 5s 5c 8h': ((2, 2, 1), (10, 5, 8)),\n", " 'Ts Tc 5s 5c 7h': ((2, 2, 1), (10, 5, 7)),\n", " '9s 9c 8s 8c As': ((2, 2, 1), (9, 8, 14)),\n", " '3s 3c 2s 2d Ah': ((2, 2, 1), (3, 2, 14)),\n", " 'As Ac 4c 5s 6s': ((2, 1, 1, 1), (14, 6, 5, 4)),\n", " '4s 4c As Ks Qs': ((2, 1, 1, 1), (4, 14, 13, 12)),\n", " '4h 4d Kh Qd Jd': ((2, 1, 1, 1), (4, 13, 12, 11)),\n", " '2d 2c Ad Kd Qd': ((2, 1, 1, 1), (2, 14, 13, 12)),\n", " 'Ah 3s 4s 5s 6s': ((1, 1, 1, 1, 1), (14, 6, 5, 4, 3)),\n", " 'Kh Qh Jh Th 8d': ((1, 1, 1, 1, 1), (13, 12, 11, 10, 8)),\n", " '7d 2s 4s 5s 6s': ((1, 1, 1, 1, 1), (7, 6, 5, 4, 2)),\n", " '7h 6s 5d 3s 2d': ((1, 1, 1, 1, 1), (7, 6, 5, 3, 2))}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "{join(h): ranking(h) for h in hands}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Alternative Definition: `ranking2`\n", "\n", "If you think dealing with integer partitions of 5 was a little too abstract, and/or that fabricating `(3, 1, 3)` as the type for a flush was a little too *ad hoc*, then here's an alternative ranking function, `ranking2`, that you might find more straightforward. Instead of type tuples, it uses the integers 9 to 0 for types, and then does tiebreakers in the same way as `ranking`.\n", "\n", "The one tricky part is the use of `kinds/kind`: for a two-pair hand `8h 9c 8d 9d 4h`, `kinds` will be the list `[[], [4], [9, 8], [], [], []]`, and `kinds[2]` is `[9, 8]` (meaning: what ranks have 2 of a kind? 9 and 8, with the highest rank always first), and `kind(1)` is `4` (meaning: what rank has one of a kind? 4). " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def ranking2(hand) -> tuple:\n", " \"\"\"Return a value indicating how high the hand ranks.\"\"\"\n", " ranks = sorted(map(rank, hand), reverse=True)\n", " if ranks == [14, 5, 4, 3, 2]:\n", " ranks = [5, 4, 3, 2, 1]\n", " straight = len(set(ranks)) == 5 and max(ranks) - min(ranks) == 4\n", " flush = the_same(map(suit, hand))\n", " kinds = [[] for n in range(6)]\n", " kind = lambda n: kinds[n][0]\n", " for r in sorted(set(ranks), reverse=True):\n", " kinds[ranks.count(r)].append(r)\n", " return ((9, max(ranks)) if kinds[5] else\n", " (8, max(ranks)) if straight and flush else\n", " (7, kind(4), kind(1)) if kinds[4] else\n", " (6, kind(3), kind(2)) if kinds[3] and kinds[2] else\n", " (5, *ranks) if flush else\n", " (4, max(ranks)) if straight else\n", " (3, kind(3), *kinds[1]) if kinds[3] else\n", " (2, *kinds[2], kind(1)) if len(kinds[2]) == 2 else\n", " (1, kind(2), *kinds[1]) if kinds[2] else\n", " (0, *ranks))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`ranking2` passes the tests:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "40" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test(ranking2, hands)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are what the `ranking2` rankings look like—a little more concise:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'As Ac Ah Ad Ad': (9, 14),\n", " 'Kh Kd Ks Kc Kh': (9, 13),\n", " '3h 3s 3d 3c 3c': (9, 3),\n", " '2s 2c 2d 2h 2h': (9, 2),\n", " 'As Ks Qs Ts Js': (8, 14),\n", " 'Kc Qc Jc Tc 9c': (8, 13),\n", " '6d 5d 4d 3d 2d': (8, 6),\n", " '5h 4h 3h 2h Ah': (8, 5),\n", " 'As Ac Ad Ah 2s': (7, 14, 2),\n", " '7s 7c 7d 2d 7h': (7, 7, 2),\n", " '6s 6c 6d 6h 9s': (7, 6, 9),\n", " 'As 5h 5c 5d 5s': (7, 5, 14),\n", " 'Th Tc Td 5h 5c': (6, 10, 5),\n", " '9h 9c 9d 8c 8h': (6, 9, 8),\n", " '6h 6c 6d Tc Th': (6, 6, 10),\n", " '5c 5d 5s As Ah': (6, 5, 14),\n", " 'As 2s 3s 4s 6s': (5, 14, 6, 4, 3, 2),\n", " 'Kc Qc Jc Tc 2c': (5, 13, 12, 11, 10, 2),\n", " 'Qc Jc Tc 9c 7c': (5, 12, 11, 10, 9, 7),\n", " '4h 5h 6h 7h 9h': (5, 9, 7, 6, 5, 4),\n", " 'As Kd Qc Td Jh': (4, 14),\n", " 'Kc Qh Jd Th 9c': (4, 13),\n", " '6c 5d 4h 3s 2s': (4, 6),\n", " 'As 2d 3c 4h 5s': (4, 5),\n", " 'As Ac Ad 2h 3h': (3, 14, 3, 2),\n", " 'Ts Tc Th 9s 8c': (3, 10, 9, 8),\n", " 'Ts Tc Th 9s 7c': (3, 10, 9, 7),\n", " '9h 9s 9d Ah Kh': (3, 9, 14, 13),\n", " 'Ts Tc 5s 5c 8h': (2, 10, 5, 8),\n", " 'Ts Tc 5s 5c 7h': (2, 10, 5, 7),\n", " '9s 9c 8s 8c As': (2, 9, 8, 14),\n", " '3s 3c 2s 2d Ah': (2, 3, 2, 14),\n", " 'As Ac 4c 5s 6s': (1, 14, 6, 5, 4),\n", " '4s 4c As Ks Qs': (1, 4, 14, 13, 12),\n", " '4h 4d Kh Qd Jd': (1, 4, 13, 12, 11),\n", " '2d 2c Ad Kd Qd': (1, 2, 14, 13, 12),\n", " 'Ah 3s 4s 5s 6s': (0, 14, 6, 5, 4, 3),\n", " 'Kh Qh Jh Th 8d': (0, 13, 12, 11, 10, 8),\n", " '7d 2s 4s 5s 6s': (0, 7, 6, 5, 4, 2),\n", " '7h 6s 5d 3s 2d': (0, 7, 6, 5, 3, 2)}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "{join(h): ranking2(h) for h in hands}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Timing\n", "\n", "Below we see that `ranking2` is about 15% faster than `ranking`, so use `ranking2` when speed is important." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "31.1 ms ± 4.34 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "27.1 ms ± 3.48 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "%timeit test(ranking, hands)\n", "%timeit test(ranking2, hands)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Winning\n", "\n", "It is tempting to define a function `winner(hands) -> Hand` to see who wins a round, and we will do that, for convenience. But in poker if two hands are exactly the same except for suits, then they tie. So to be more precise we will also define `winners(hand)` to return a *set* of winners (which in most cases will have only one member).\n", "\n", "Note that **ranking** is the fundamental idea, and **winning** is determined by ranking. Some programmers try to answer the question of whether one hand is better than another by defining `better(hand1, hand2)`. They quickly find that it is hopelessly complex to be considering two hands at the same time, asking things like \"this hand has three of a kind; does the other hand have anything better?\" It is much simpler to compare rankings. \n", "\n", "Also note that using the word **ranking** rather than **scoring** is deliberate. If we were doing scoring, it would make sense to ask for an average score for a set of hands. With ranking, we can sort hands from best to worst, but it makes no sense to say a straight (type 4) and a straight flush (type 8) average out to a full house (type 6).\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def winner(hands) -> Hand: \n", " \"\"\"One of the winning hands (there might be others).\"\"\"\n", " return max(hands, key=ranking2)\n", "\n", "def winners(hands) -> set:\n", " \"\"\"The set of hands with the best ranking.\"\"\"\n", " best = max(map(ranking2, hands))\n", " return {h for h in hands if ranking2(h) == best}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Type Names\n", "\n", "So far, the names of the types (e.g. \"full house\") have appeared only in prose, not in the code. Let's fix that. We'll take advantage of the fact that the first element of the tuple returned by `ranking2` is an integer ranging from 0 to 9 that can be used as an index into a tuple of `type_names`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "types = range(10)\n", "\n", "type_names = ('high card', 'one pair', 'two pair', 'three of a kind', 'straight',\n", " 'flush', 'full house', 'four of a kind', 'straight flush', 'five of a kind')\n", "\n", "def type_name(hand) -> str: return type_names[ranking2(hand)[0]]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'As Ac Ah Ad Ad': 'five of a kind',\n", " 'Kh Kd Ks Kc Kh': 'five of a kind',\n", " '3h 3s 3d 3c 3c': 'five of a kind',\n", " '2s 2c 2d 2h 2h': 'five of a kind',\n", " 'As Ks Qs Ts Js': 'straight flush',\n", " 'Kc Qc Jc Tc 9c': 'straight flush',\n", " '6d 5d 4d 3d 2d': 'straight flush',\n", " '5h 4h 3h 2h Ah': 'straight flush',\n", " 'As Ac Ad Ah 2s': 'four of a kind',\n", " '7s 7c 7d 2d 7h': 'four of a kind',\n", " '6s 6c 6d 6h 9s': 'four of a kind',\n", " 'As 5h 5c 5d 5s': 'four of a kind',\n", " 'Th Tc Td 5h 5c': 'full house',\n", " '9h 9c 9d 8c 8h': 'full house',\n", " '6h 6c 6d Tc Th': 'full house',\n", " '5c 5d 5s As Ah': 'full house',\n", " 'As 2s 3s 4s 6s': 'flush',\n", " 'Kc Qc Jc Tc 2c': 'flush',\n", " 'Qc Jc Tc 9c 7c': 'flush',\n", " '4h 5h 6h 7h 9h': 'flush',\n", " 'As Kd Qc Td Jh': 'straight',\n", " 'Kc Qh Jd Th 9c': 'straight',\n", " '6c 5d 4h 3s 2s': 'straight',\n", " 'As 2d 3c 4h 5s': 'straight',\n", " 'As Ac Ad 2h 3h': 'three of a kind',\n", " 'Ts Tc Th 9s 8c': 'three of a kind',\n", " 'Ts Tc Th 9s 7c': 'three of a kind',\n", " '9h 9s 9d Ah Kh': 'three of a kind',\n", " 'Ts Tc 5s 5c 8h': 'two pair',\n", " 'Ts Tc 5s 5c 7h': 'two pair',\n", " '9s 9c 8s 8c As': 'two pair',\n", " '3s 3c 2s 2d Ah': 'two pair',\n", " 'As Ac 4c 5s 6s': 'one pair',\n", " '4s 4c As Ks Qs': 'one pair',\n", " '4h 4d Kh Qd Jd': 'one pair',\n", " '2d 2c Ad Kd Qd': 'one pair',\n", " 'Ah 3s 4s 5s 6s': 'high card',\n", " 'Kh Qh Jh Th 8d': 'high card',\n", " '7d 2s 4s 5s 6s': 'high card',\n", " '7h 6s 5d 3s 2d': 'high card'}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "{join(h): type_name(h) for h in hands}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Poker Variants\n", "\n", "There are many variants of poker. In some variants a player makes a hand by combining face-down private cards that they hold, called **hole cards**, with face-up shared cards in the middle of the table called **community cards**. Let's look at a few variants from the point of view of card ranking. Again, we'll ignore the betting parts.\n", "\n", "# Texas Hold 'Em Poker\n", "\n", "In Texas Hold 'Em, one of the most popular games, each player has two hole cards, and everyone shares five community cards. A player can choose any subset of the seven cards for their hand. \n", "\n", "Finding the best hand from among the hole and community cards is straightforward: just try every combination and choose a hand with the maximum ranking (that's what the function `winner` does):" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def texas_best(hole, community) -> Hand:\n", " \"\"\"Find the best hand from any selection of 2 hole and 5 community cards.\"\"\"\n", " assert len(hole) == 2 and len(community) == 5\n", " return winner(combinations(hole + community, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A player can make a full house with the following hole cards and community cards:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('As', '7d', 'Ac', 'Ah', '7c')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "texas_best(cards('As 7d'), cards('Ac 8d 9d Ah 7c'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Omaha Hold 'Em Poker\n", "\n", "In Omaha Hold 'Em, players get four hole cards, and they have to use exactly two of them, plus three of the five community cards, to make their hand. Not quite as straightforward to find the best hand:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def omaha_best(hole, community) -> Hand:\n", " \"\"\"Find the best hand using 2 of 4 hole cards and 3 of 5 community cards.\"\"\"\n", " assert len(hole) == 4 and len(community) == 5\n", " return winner(h2 + c3 for h2 in combinations(hole, 2) \n", " for c3 in combinations(community, 3))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('As', '7d', 'Ac', 'Ah', '7c')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "omaha_best(cards('As 7d 6d 5d'), cards('Ac 8d 9d Ah 7c'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following hand, the hole plus community cards include a royal straight flush and a fives-over-tens full house. But neither of these hands can be made because the hand must have exactly two hole cards, so we end up with three fives." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('Ad', '5d', 'Jd', '5c', '5h')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "omaha_best(cards('Ad Kd Qd 5d'), cards('Jd Td Tc 5c 5h'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Three-D Poker\n", "\n", "In this novel variant, due to Bram Cohen, there are two hole cards and 15 community cards arranged in three sets of five. Players must use their two hole cards, plus exactly one card from each of the three sets of community cards." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def threeD_best(hole, community) -> Hand:\n", " \"\"\"Find the best hand from the 2 hole cards plus 1 each from \n", " the first 5, middle 5, and last 5 of the 15 community cards.\"\"\"\n", " assert len(hole) == 2 and len(community) == 15\n", " A, B, C = community[0:5], community[5:10], community[10:15]\n", " return winner([*hole, *c3] for c3 in product(A, B, C))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example below, the player has a queen hole card and there are two queen community cards, but they are in the same set, so the player could only get one of them. It turns out that it is better to give up on the idea of three queens and take two kings and a 4 instead, making two pair." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Qh', '4s', 'Kd', '4h', 'Kc']" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "threeD_best(cards('Qh 4s'), cards('Qs Qc Kd Ts Ah 5d 4h 7s 9c 6s 5h 7c Kc Kh 9h'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Joker Poker\n", "\n", "Sometimes in friendly house games, a joker or two is added to the deck. A joker can serve as any card the player wants it to be, including a card already in their hand (that's how you get five-of-a-kind)." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "joker ='**'\n", "deck = [r + s for r in rankstr for s in 'cdhs'] # Regular Deck of 52 cards\n", "jdeck = [joker, joker, *deck] # Deck of 54 cards including two jokers\n", "\n", "def joker_best(hand) -> Hand:\n", " \"\"\"Find the best hand by replacing any jokers with any other card.\"\"\"\n", " replacements = [deck if card == joker else [card] for card in hand]\n", " return winner(product(*replacements))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('8d', '8c', '7d', '6d', '8s')" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joker_best(cards('8d ** 7d 6d 8s')) # One Joker used to make three of a kind" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('8d', '9d', '7d', '6d', 'Td')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joker_best(cards('8d ** 7d 6d **')) # Two Jokers used to make a straight flush" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the poker variants we have seen so far, even joker poker, were solved by finding a hand with maximum ranking out of all the possible hands that could be made with community cards or jokers. There was always a clear-cut way to do this, and one definitive best hand. Next we will see a variant where that is no longer true.\n", "\n", "# Draw Poker\n", "\n", "In draw poker players are given the option to **discard** one or more card(s) and **replace** them with new one(s) drawn from the deck. But unlike in joker poker where you get to choose the replacement, in draw poker you're at the mercy of the luck of the draw. \n", "\n", "In draw poker there is no **best hand** that you can make with certainty; instead we need to find the **best action** (which card to discard), with the understanding that the action will sometimes work (we hit that straight) and sometimes not. The best action is determined by the best **average** over all possible outcomes of the action (i.e. all possible draws of cards).\n", "\n", "We could try to find the action that maximizes the average type number of the resulting hands. But that's not quite right: there's a big advantage in going from type 1 (one pair) to type 3 (three of a kind), because that very often makes the difference between losing and winning. There is less of an advantage in going from type 7 (four of a kind) to type 9 (five of a kind) because the four of a kind will win most of the time anyway. \n", "\n", "# Win Probability\n", "\n", "What we really want to do is **maximize the expected probability of winning**. The probability of winning depends on many things: the number of other players, the community cards if any, the other rules of the game (e.g. are draws or jokers allowed), the skill of the other players, the hints that the other players have given about their cards through their bets and physical tells, etc.\n", "\n", "We'll simplify by ignoring most of these factors. We'll assume **heads up** poker, in which there are only two players. A hand's probability of winning is proportional to the percentage of hands that it is better than, out of all hands. Wikipedia [says](https://en.wikipedia.org/wiki/Poker_probability) that 50.1% of all hands are \"high card\", so we might say that a \"high card\" hand has a 50.1% probability of winning. But that can't be the full story, because an ace high card has a much better chance of winning than a 7 high card. So we'll estimate that the probability of a hand winning is a linear interpolation, based on the value of the high card in the hand, between the cumulative probability of the hand's type and the cumulative probability of one type lower. (This assumption is wrong, again, because the relationship is not exactly linear (e.g. the lowest possible high card for type 0 is actually a 7, because otherwise the hand would be a straight) and because non-high cards also come into play, but maybe it is a close enough approximation.) I'll use Wikipedia's [Frequency of 5-card poker hands](https://en.wikipedia.org/wiki/Poker_probability) to define `deal_win_P`, the cummulative probabilities keyed by hand type numbers:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "def cumsum(numbers, normalize=True) -> list:\n", " \"\"\"cumsum(numbers)[i] gives the cumulative sum of the first n numbers, \n", " optionally normalized to sum to 1.\"\"\"\n", " denom = sum(numbers) if normalize else 1\n", " return [sum(numbers[:n]) / denom for n in range(len(numbers) + 1)]\n", "\n", "deal_win_P = cumsum([1302540, 1098240, 123552, 54912, 10200, 5108, 3744, 624, 40, 0])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.0,\n", " 0.5011773940345369,\n", " 0.9237464216455813,\n", " 0.9712854372518238,\n", " 0.992413888632376,\n", " 0.9963385354141656,\n", " 0.9983039369593991,\n", " 0.9997445131898913,\n", " 0.9999846092283067,\n", " 1.0,\n", " 1.0]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "deal_win_P " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With that I can define `win_probability`:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def win_probability(hand, P=deal_win_P) -> float:\n", " \"\"\"Approximate win probability for a hand, given a list, P, of win probabilities for types.\"\"\"\n", " type, hi, *_ = ranking2(hand)\n", " return P[type] + (hi - 1) / 13 * (P[type + 1] - P[type])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are win probabilities for all the test hands, expressed in percents. The `deal_win_P` probabilities assume no wild cards, and thus no five-of-a-kind; therefore an ace-high straight flush (royal straight flush) is the highest possible hand, and gets a 100% win probability." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "As Ac Ah Ad Ad 100.00000% five of a kind\n", "Kh Kd Ks Kc Kh 100.00000% five of a kind\n", "3h 3s 3d 3c 3c 100.00000% five of a kind\n", "2s 2c 2d 2h 2h 100.00000% five of a kind\n", "As Ks Qs Ts Js 100.00000% straight flush\n", "Kc Qc Jc Tc 9c 99.99988% straight flush\n", "6d 5d 4d 3d 2d 99.99905% straight flush\n", "5h 4h 3h 2h Ah 99.99893% straight flush\n", "As Ac Ad Ah 2s 99.99846% four of a kind\n", "7s 7c 7d 2d 7h 99.98553% four of a kind\n", "6s 6c 6d 6h 9s 99.98369% four of a kind\n", "As 5h 5c 5d 5s 99.98184% four of a kind\n", "Th Tc Td 5h 5c 99.93013% full house\n", "9h 9c 9d 8c 8h 99.91904% full house\n", "6h 6c 6d Tc Th 99.88580% full house\n", "5c 5d 5s As Ah 99.87472% full house\n", "As 2s 3s 4s 6s 99.83039% flush\n", "Kc Qc Jc Tc 2c 99.81528% flush\n", "Qc Jc Tc 9c 7c 99.80016% flush\n", "4h 5h 6h 7h 9h 99.75480% flush\n", "As Kd Qc Td Jh 99.63385% straight\n", "Kc Qh Jd Th 9c 99.60366% straight\n", "6c 5d 4h 3s 2s 99.39234% straight\n", "As 2d 3c 4h 5s 99.36215% straight\n", "As Ac Ad 2h 3h 99.24139% three of a kind\n", "Ts Tc Th 9s 8c 98.59128% three of a kind\n", "Ts Tc Th 9s 7c 98.59128% three of a kind\n", "9h 9s 9d Ah Kh 98.42876% three of a kind\n", "Ts Tc 5s 5c 8h 95.66580% two pair\n", "Ts Tc 5s 5c 7h 95.66580% two pair\n", "9s 9c 8s 8c As 95.30012% two pair\n", "3s 3c 2s 2d Ah 93.10601% two pair\n", "As Ac 4c 5s 6s 92.37464% one pair\n", "4s 4c As Ks Qs 59.86933% one pair\n", "4h 4d Kh Qd Jd 59.86933% one pair\n", "2d 2c Ad Kd Qd 53.36827% one pair\n", "Ah 3s 4s 5s 6s 50.11774% high card\n", "Kh Qh Jh Th 8d 46.26253% high card\n", "7d 2s 4s 5s 6s 23.13126% high card\n", "7h 6s 5d 3s 2d 23.13126% high card\n" ] } ], "source": [ "for h in hands:\n", " print(f'{join(h)} {win_probability(h):10.5%} {type_name(h)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do you really expect to win 50% of the time with a high card ace, or 60% of the time with a pair of fours? Not in a game with community cards, or draws, or jokers, or more than two players. But if we're asking \"*two players just got dealt five cards each; what's the probability that my five cards are better than the others?*\" then these numbers are approximately right. If we were playing a different game, we would pass in a different probability list `P` to `win_probability`.\n", "\n", "Now we can \"solve\" draw poker. In our simplified variant players are allowed to replace one card only. (It is possible that `nocard` is the best action, e.g. if you already have a full house, straight, or flush.) `draw_best_action` returns the action that maximizes the expected value of the resulting hand. That is, it finds the card that, when discarded, maximizes the expected (mean) win probability over all possible replacement cards. It is traditional to use `E` for expected value. " ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "def draw_best_action(hand, P=deal_win_P) -> Card:\n", " \"\"\"What card should you discard (or None) from this hand?\"\"\"\n", " def E(c): return mean(win_probability(h, P) for h in discard_outcomes(c, hand))\n", " return max([nocard, *hand], key=E)\n", "\n", "def discard_outcomes(card, hand) -> list:\n", " \"\"\"All the hands that result from replacing `card` in `hand` with a new card from the `deck`.\"\"\"\n", " return ([hand] if not card else\n", " [replace(card, new, hand)\n", " for new in deck if new not in hand])\n", "\n", "def replace(old, new, alist) -> list:\n", " \"\"\"Replace old with new in alist.\"\"\"\n", " return [new if c == old else c for c in alist]\n", "\n", "nocard = ''" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2d'" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "draw_best_action(cards('2d 7d 4d 9d As'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This says that with the given hand the best action is to discard the low card, the 2 of diamonds. Drawing a new card could give us a pair of 4s, 7s, 9s, or aces, or it could leave us with only an ace high. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gathering Statistics for Type Probabilities\n", "\n", "I would like to gather some hand-type probabilities, like [Wikipedia's probabilities](https://en.wikipedia.org/wiki/Poker_probability), for different game variants. When we are just dealing five cards, it is easy enough to use combinatorics to compute exactly the proportion of hands of each type. But with complex games involving drawing from the deck or choosing community cards it is easier to estimate probabilities by doing a simulation.\n", "\n", "I'll use the notion of a **handmaker**: a function of zero arguments (except maybe optional parameters) that when called returns a random hand, according to the best possible strategy. So `texas_handmaker` deals seven cards, uses `split` to split them into 2 hole cards and 5 community cards, and then determines the best hand that can be made from those cards with `texas_best`. The other handmaker functions do likewise. The `draw_handmaker` samples 5 cards, decides what the optimal action is, and then draws a random card to replace the discard." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "def deal_handmaker() -> Hand: return deal(5)\n", "def texas_handmaker() -> Hand: return texas_best(*deal(2, 5))\n", "def omaha_handmaker() -> Hand: return omaha_best(*deal(4, 5))\n", "def joker_handmaker() -> Hand: return joker_best(deal(5, deck=jdeck))\n", "def threeD_handmaker() -> Hand: return threeD_best(*deal(2, 15))\n", "\n", "def draw_handmaker(P=deal_win_P) -> Hand:\n", " hand = deal(5)\n", " discard = draw_best_action(hand, P)\n", " return replace(discard, draw_card(deck, butnot=hand), hand)\n", "\n", "handmakers = (deal_handmaker, draw_handmaker, joker_handmaker, \n", " texas_handmaker, omaha_handmaker, threeD_handmaker)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def deal(hole, community=0, deck=deck): \n", " cards = random.sample(deck, hole + community)\n", " return cards if not community else (cards[:hole], cards[hole:])\n", "\n", "def draw_card(deck, butnot=()) -> Card:\n", " \"\"\"Randomly draw a card from deck, but make sure it is not in `butnot`.\"\"\"\n", " while True:\n", " card = random.choice(deck)\n", " if card not in butnot:\n", " return card" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're ready to run a simulation. The function `estimated_type_frequency` runs a handmaker repeatedly and returns a counter of frequencies for hand types. We use a `lru_cache` decorator so that we don't wait a long time if we repeat a calculation in a cell farther down in the notebook." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "@lru_cache(None)\n", "def estimated_type_frequency(handmaker, n) -> Counter:\n", " \"\"\"A counter of hand type names for n hands made by handmaker.\"\"\"\n", " return Counter(type_name(handmaker()) for _ in range(n))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({'one pair': 424,\n", " 'high card': 490,\n", " 'flush': 4,\n", " 'three of a kind': 16,\n", " 'two pair': 56,\n", " 'full house': 3,\n", " 'straight': 6,\n", " 'four of a kind': 1})" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "estimated_type_frequency(deal_handmaker, 1000)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def stats(handmakers, n=10**5):\n", " \"\"\"Print hand type probability statistics for each handmaker.\"\"\"\n", " samples = [estimated_type_frequency(h, n) for h in handmakers]\n", " def name(h): return h.__name__.split('_')[0]\n", " print(f'{\"Type of Hand\":15} {join(f\"{name(h):>7s}\" for h in handmakers)}')\n", " print(f'{\"-\"*15} {join(\"-\"*7 for h in handmakers)}')\n", " for t in reversed(type_names):\n", " print(f'{t:15} ', join(f'{s[t]/n:7.3%}' for s in samples))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Type of Hand deal draw joker texas omaha threeD\n", "--------------- ------- ------- ------- ------- ------- -------\n", "five of a kind 0.000% 0.000% 0.003% 0.000% 0.000% 0.000%\n", "straight flush 0.002% 0.009% 0.010% 0.023% 0.095% 0.189%\n", "four of a kind 0.031% 0.077% 0.309% 0.160% 0.478% 1.558%\n", "full house 0.150% 0.694% 0.290% 2.551% 6.268% 9.729%\n", "flush 0.174% 0.786% 0.367% 3.169% 6.796% 7.679%\n", "straight 0.400% 1.314% 1.047% 4.615% 11.416% 18.565%\n", "three of a kind 1.935% 3.670% 7.294% 4.747% 8.743% 25.989%\n", "two pair 4.692% 9.980% 3.930% 23.456% 36.949% 28.106%\n", "one pair 42.378% 47.771% 45.501% 43.810% 26.305% 8.178%\n", "high card 50.238% 35.699% 41.249% 17.469% 2.950% 0.007%\n", "CPU times: user 5min 20s, sys: 1.91 s, total: 5min 22s\n", "Wall time: 5min 52s\n" ] } ], "source": [ "%time stats(handmakers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `deal` column shows good agreement with the [known poker probabilities](https://en.wikipedia.org/wiki/Poker_probability). The other columns show that hands get better as we allow more options in the game, with Omaha yielding much higher hands than Texas, but Three-D yielding the highest hands of all. There is an inversion in Omaha where straights are more probable than three of a kind. It appears that is because many of the three-of-a-kinds become full houses (which are just three-of-a-kinds where the other two cards happen to match). In Three-D, there is an inversion in that full houses are more probable than flushes; perhaps a flush should beat a full house in that game.\n", "\n", "# Draw Poker Revisited\n", "\n", "Now that we have seen the stats for what types of hands we can achieve in draw poker by following `draw_best_action`, it makes sense that we modify the best actions! That is, `draw_best_action` assumed that \"high card\" wins about 50% of the time. But we see in the `draw` column of the stats chart that if players use their draw card properly, a high card will only win about 35% of hands. That should change our actions, making us more willing to take risks to get a pair or higher. \n", "\n", "The new cumulative winning percentages for draw poker, assuming players will chose the best action for their draw, can be computed as follows:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.0,\n", " 0.35699,\n", " 0.8347,\n", " 0.9345,\n", " 0.9712,\n", " 0.98434,\n", " 0.9922,\n", " 0.99914,\n", " 0.99991,\n", " 1.0,\n", " 1.0]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = estimated_type_frequency(draw_handmaker, 10**5)\n", "draw_win_P = cumsum([f[type_names[type]] for type in types])\n", "draw_win_P" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's consider the following hand under both sets of probabilities:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'4s'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hand = cards('6d 8c 4s Qc 7s')\n", "\n", "draw_best_action(hand, P=deal_win_P)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Under the old `deal_win_P` probabilities, the best play is the safe play: discard the low card. We'll end up with either a pair or at least a queen high.\n", "\n", "But under the new `draw_win_P` probabilities, the best action has changed:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Qc'" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "draw_best_action(hand, P=draw_win_P)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a risky play: we give up our high card, so our hand could get worse. But there's a 4/47 chance of greatly improving the hand by hitting the inside straight; the same 16/47 chance of getting a pair of some kind (but not a pair of queens), and a 11/47 chance of getting a hiugh card of queen or better. The difference in actions is because under `draw_win_P`, a queen high is much less likely to win, so it is worth sacrificing the queen for a chance at a straight.\n", "\n", "Now I will define a new handmaker that uses these probabilities, and I will regenerate the stats chart (note that the `@lru_cache` dedcorator on `estimated_type_frequency` means that I won't have to wait another five minutes)." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Type of Hand draw2 deal draw joker texas omaha threeD\n", "--------------- ------- ------- ------- ------- ------- ------- -------\n", "five of a kind 0.000% 0.000% 0.000% 0.003% 0.000% 0.000% 0.000%\n", "straight flush 0.003% 0.002% 0.009% 0.010% 0.023% 0.095% 0.189%\n", "four of a kind 0.076% 0.031% 0.077% 0.309% 0.160% 0.478% 1.558%\n", "full house 0.696% 0.150% 0.694% 0.290% 2.551% 6.268% 9.729%\n", "flush 0.810% 0.174% 0.786% 0.367% 3.169% 6.796% 7.679%\n", "straight 1.404% 0.400% 1.314% 1.047% 4.615% 11.416% 18.565%\n", "three of a kind 3.723% 1.935% 3.670% 7.294% 4.747% 8.743% 25.989%\n", "two pair 9.533% 4.692% 9.980% 3.930% 23.456% 36.949% 28.106%\n", "one pair 47.700% 42.378% 47.771% 45.501% 43.810% 26.305% 8.178%\n", "high card 36.055% 50.238% 35.699% 41.249% 17.469% 2.950% 0.007%\n" ] } ], "source": [ "def draw2_handmaker() -> Hand: return draw_handmaker(P=draw_win_P)\n", "\n", "stats((draw2_handmaker, *handmakers))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To be truly accurate, we should repeat this process of using updated probabilities until we hit a fixpoint. But we can see that there is not that much difference between the `draw` and the `draw2` columns, so I'm not going to bother with that.\n", "\n", "Below I compare the two win probabilities for the test hands, `deal_win_P` on the left and `draw_win_P` on the right:\n", "\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "As Ac Ah Ad Ad 100.00000% 100.00000% five of a kind\n", "Kh Kd Ks Kc Kh 100.00000% 100.00000% five of a kind\n", "3h 3s 3d 3c 3c 100.00000% 100.00000% five of a kind\n", "2s 2c 2d 2h 2h 100.00000% 100.00000% five of a kind\n", "As Ks Qs Ts Js 100.00000% 100.00000% straight flush\n", "Kc Qc Jc Tc 9c 99.99988% 99.99931% straight flush\n", "6d 5d 4d 3d 2d 99.99905% 99.99446% straight flush\n", "5h 4h 3h 2h Ah 99.99893% 99.99377% straight flush\n", "As Ac Ad Ah 2s 99.99846% 99.99100% four of a kind\n", "7s 7c 7d 2d 7h 99.98553% 99.94954% four of a kind\n", "6s 6c 6d 6h 9s 99.98369% 99.94362% four of a kind\n", "As 5h 5c 5d 5s 99.98184% 99.93769% four of a kind\n", "Th Tc Td 5h 5c 99.93013% 99.70046% full house\n", "9h 9c 9d 8c 8h 99.91904% 99.64708% full house\n", "6h 6c 6d Tc Th 99.88580% 99.48692% full house\n", "5c 5d 5s As Ah 99.87472% 99.43354% full house\n", "As 2s 3s 4s 6s 99.83039% 99.22000% flush\n", "Kc Qc Jc Tc 2c 99.81528% 99.15954% flush\n", "Qc Jc Tc 9c 7c 99.80016% 99.09908% flush\n", "4h 5h 6h 7h 9h 99.75480% 98.91769% flush\n", "As Kd Qc Td Jh 99.63385% 98.43400% straight\n", "Kc Qh Jd Th 9c 99.60366% 98.33292% straight\n", "6c 5d 4h 3s 2s 99.39234% 97.62538% straight\n", "As 2d 3c 4h 5s 99.36215% 97.52431% straight\n", "As Ac Ad 2h 3h 99.24139% 97.12000% three of a kind\n", "Ts Tc Th 9s 8c 98.59128% 95.99077% three of a kind\n", "Ts Tc Th 9s 7c 98.59128% 95.99077% three of a kind\n", "9h 9s 9d Ah Kh 98.42876% 95.70846% three of a kind\n", "Ts Tc 5s 5c 8h 95.66580% 90.37923% two pair\n", "Ts Tc 5s 5c 7h 95.66580% 90.37923% two pair\n", "9s 9c 8s 8c As 95.30012% 89.61154% two pair\n", "3s 3c 2s 2d Ah 93.10601% 85.00538% two pair\n", "As Ac 4c 5s 6s 92.37464% 83.47000% one pair\n", "4s 4c As Ks Qs 59.86933% 46.72308% one pair\n", "4h 4d Kh Qd Jd 59.86933% 46.72308% one pair\n", "2d 2c Ad Kd Qd 53.36827% 39.37369% one pair\n", "Ah 3s 4s 5s 6s 50.11774% 35.69900% high card\n", "Kh Qh Jh Th 8d 46.26253% 32.95292% high card\n", "7d 2s 4s 5s 6s 23.13126% 16.47646% high card\n", "7h 6s 5d 3s 2d 23.13126% 16.47646% high card\n" ] } ], "source": [ "for h in hands:\n", " p1, p2 = win_probability(h, P=deal_win_P), win_probability(h, P=draw_win_P)\n", " print(f'{join(h)} {p1:10.5%} {p2:10.5%} {type_name(h)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "______\n", "\n", "# What Comes First?\n", "\n", "Bram Cohen suggests an interesting experiment: shuffle a deck and flip over cards one at a time, until either a flush or a straight appears among the face-up cards. Among five-card hands, a straight is more likely, so shouldn't it come up first more often? Let's create a simulation to see:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def flush_or_straight_first(deck=deck) -> str:\n", " \"\"\"Deal cards randomly until a straight or flush show up among any of the cards.\"\"\"\n", " random.shuffle(deck)\n", " suits = Counter() # Count how many we have of each suit\n", " rankset = set() # Set of all the ranks of the cards so far\n", " first = '' # What came first?\n", " for card in deck:\n", " suits[suit(card)] += 1\n", " rankset.add(rank(card))\n", " if is_straight(rankset): first += 'straight'\n", " if 5 in suits.values(): first += 'flush'\n", " if first: \n", " return first\n", " \n", "def is_straight(rankset) -> bool:\n", " \"\"\"See if any possible straight is a subset of the rankset.\"\"\"\n", " return any(straight.issubset(rankset) for straight in straights)\n", "\n", "straights = [set(range(start, start + 5)) for start in range(2, 11)] + [{14, 2, 3, 4, 5}]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'flush'" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flush_or_straight_first()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'flush'" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flush_or_straight_first()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({'straight': 3961, 'flush': 5066, 'straightflush': 973})" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter(flush_or_straight_first() for _ in range(10**4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We know that a straight is a more probable hand than a flush in general, but\n", "counterintuitively, a flush is more likely to come up first, by about 50% to 40%, with about 10% of the time both of them coming up on the same card (a straight flush). One explanation for this is the [pigeonhole principle](https://en.wikipedia.org/wiki/Pigeonhole_principle): there are only four suits (pigeonholes), each of which can only fit four cards (pigeons) before we get a flush. So that means, in the worst case, the first 16 cards fill all the holes, and the 17th card **must** make a flush. But for a straight, it is possible to go 20 or 30 or even 40 cards without making any straight (e.g.: 2-3-4-5, 7-8-9-T, and Q-K of each suit is 40 cards with no straight).\n", " \n", "We can extend Bram's experiment to all the types. The function `what_first` simulates a single pass through the deck, flipping up cards until every type has appeared, and returning as a result a dict of `{type_number: number_of_cards_to_make_that_type}`. We'll leave out type 0, \"high card\", because the very first card always makes a high card." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "def what_first(deck=deck) -> list:\n", " \"\"\"Flip cards; record how many cards it took for each type.\"\"\"\n", " random.shuffle(deck)\n", " suits = Counter() # Count how many we have of each suit\n", " ranks = Counter() # Count how many we have of each rank\n", " rankset = set() # Set of all the ranks of the cards so far\n", " ranksets = {s: set() for s in 'shdc'} # As above, but per suit\n", " numbers = {} # The number of cards required to make each type\n", " kind = lambda n: sum(ranks[r] >= n for r in ranks)\n", " def record(type, i): \n", " if type not in numbers: numbers[type] = i\n", " for i, card in enumerate(deck, 1):\n", " suits[suit(card)] += 1\n", " ranks[rank(card)] += 1\n", " rankset |= {rank(card)}\n", " ranksets[suit(card)] |= {rank(card)}\n", " if 8 not in numbers and any(is_straight(ranksets[suit]) for suit in 'sdhc'): \n", " record(8, i) # straight flush\n", " if kind(4): record(7, i) # four of a kind \n", " if kind(3) and kind(2) >= 2: record(6, i) # full house\n", " if 5 in suits.values(): record(5, i) # flush\n", " if 4 not in numbers and is_straight(rankset): \n", " record(4, i) # straight\n", " if kind(3): record(3, i) # three of a kind\n", " if kind(2) == 2: record(2, i) # two pair\n", " if kind(2): record(1, i) # one pair\n", " if len(numbers) == 8:\n", " return numbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A sample run:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1: 3, 2: 6, 5: 11, 6: 16, 3: 16, 7: 24, 8: 26, 4: 26}" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "what_first()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I will define `what_firsts` to run `n` simulations and return a dict keyed by type number, where each value is a Counter of the card numbers where the type first appeared across all the simulations. " ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "def what_firsts(n) -> dict:\n", " \"\"\"Run what_first() n times and record numbers of cards as Counters:\n", " returns dict of {type_number: Counter(how_many_cards: how_often})}\"\"\"\n", " counters = {type: Counter() for type in types}\n", " for trial in range(n):\n", " numbers = what_first()\n", " for type in numbers:\n", " counters[type][numbers[type]] += 1\n", " return counters " ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: Counter(),\n", " 1: Counter({6: 3, 4: 2, 9: 2, 3: 1, 5: 1, 2: 1}),\n", " 2: Counter({7: 1, 11: 1, 10: 3, 12: 1, 9: 2, 4: 1, 8: 1}),\n", " 3: Counter({10: 1, 17: 1, 20: 2, 15: 2, 11: 1, 13: 1, 12: 2}),\n", " 4: Counter({8: 4, 13: 1, 9: 1, 16: 2, 11: 1, 12: 1}),\n", " 5: Counter({12: 3, 11: 1, 10: 1, 13: 3, 8: 1, 17: 1}),\n", " 6: Counter({10: 1, 17: 1, 20: 2, 15: 2, 11: 1, 13: 1, 12: 2}),\n", " 7: Counter({18: 2, 31: 1, 29: 1, 23: 1, 26: 2, 19: 1, 28: 1, 24: 1}),\n", " 8: Counter({21: 2, 29: 1, 18: 1, 24: 2, 31: 1, 30: 1, 28: 1, 26: 1}),\n", " 9: Counter()}" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "what_firsts(10) # Run 10 simulations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that type 1 (one pair) has a Counter with mostly low numbers—a pair tends to show up early. Type 8 (straight flush) has a Counter with mostly high numbers; it takes a lot of cards before you are likely to get a straight flush. \n", "\n", "Let's run 100,000 simulations:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 46.7 s, sys: 264 ms, total: 47 s\n", "Wall time: 50.3 s\n" ] } ], "source": [ "%time counters = what_firsts(10**5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The raw numbers are messy to look at, so I'll plot them, showing for each type the distribution of how many cards it took, and in the legend showing the mean number of cards for each type:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def plot_what_firsts(counters, types=range(1, 9), \n", " colors='w b g r c m y k orange'.split()):\n", " N = sum(counters[min(types)].values()) # How many trials\n", " M = max(L for c in counters.values() for L in c) # Longest trial\n", " X = range(1, M + 1) # Numbers to plot\n", " plt.figure(figsize=(10, 6))\n", " plt.xlabel('Number of cards needed')\n", " plt.ylabel(f'Percent (out of {N:,d} trials)')\n", " plt.grid(True, which='major'); \n", " plt.grid(True, which='minor', linestyle=':', alpha=0.5, markevery=1)\n", " plt.minorticks_on()\n", " for type in reversed(types):\n", " Y = [100 * counters[type][x]/ N for x in X]\n", " μ = sum(L * n for L, n in counters[type].items()) / N\n", " plt.plot(X, Y, 'o:', lw=2, alpha=0.8, color=colors[type],\n", " label=f'{type}: μ={μ:4.1f} {type_names[type]}')\n", " plt.legend()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_what_firsts(counters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, that looks kind of like a side view of the rollercoasters at Six Flags amusement park. Let's see if we can make sense of it. There seem to be three distinct patterns. First, there are three hand types with an early sharp peak: " ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_what_firsts(counters, types=(1, 2, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As discussed, the flush can go no more than 17 cards. Also by the pigeonhole principle, the pair can go no more than 14 cards (one card in each of the 13 ranks, and then the 14th card must form a pair) and two pair can go no more than 17 cards (one card in each of the 13 ranks, then 3 more cards in one of the ranks (making four of a kind), and then the 17th card must make a second pair).\n", "\n", "Next are three tightly-grouped Gaussian-looking curves. It makes sense that three of a kind and full house are close together, because usually when you get three of a kind, you have gotten some other pair previously. I'm not sure why straight is so close to them." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_what_firsts(counters, types=(3, 4, 6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, I was quite surprised to see how closely the straight flush and four of a kind curves track each other; I can't explain why:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_what_firsts(counters, types=(7, 8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That concludes our exploration of poker ranking; I hope it has inspired you to explore some on your own." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }