anand-b / poker-hand-prediction Goto Github PK

The dataset can be obtained here.
Open the Data Folder, download and extract the zip file.

1. Relevant Information:
   Each record is an example of a hand consisting of five playing
   cards drawn from a standard deck of 52. Each card is described
   using two attributes (suit and rank), for a total of 10 predictive
   attributes. There is one Class attribute that describes the
   Poker Hand. The order of cards is important, which is why there
   are 480 possible Royal Flush hands as compared to 4 (one for each
   suit explained in more detail below).

2. Number of Instances: 25010 training, 1,000,000 testing

3. Number of Attributes: 10 predictive attributes, 1 goal attribute

4. Attribute Information:

   1. S1 Suit of card #1
      Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
   2. C1 Rank of card #1
      Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
   3. S2 Suit of card #2
      Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
   4. C2 Rank of card #2
      Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
   5. S3 Suit of card #3
      Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
   6. C3 Rank of card #3
      Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
   7. S4 Suit of card #4
      Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
   8. C4 Rank of card #4
      Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
   9. S5 Suit of card #5
      Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
   10. C5 Rank of card 5

   Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 11) CLASS Poker Hand
   Ordinal (0-9)
   0: Nothing in hand; not a recognized poker hand
   1: One pair; one pair of equal ranks within five cards
   2: Two pairs; two pairs of equal ranks within five cards
   3: Three of a kind; three equal ranks within five cards
   4: Straight; five cards, sequentially ranked with no gaps
   5: Flush; five cards with the same suit
   6: Full house; pair + different rank three of a kind
   7: Four of a kind; four equal ranks within five cards
   8: Straight flush; straight + flush
   9: Royal flush; {Ace, King, Queen, Jack, Ten} + flush

5. Missing Attribute Values: None

6. Class Distribution: The first percentage in parenthesis is the representation within the training set. The second is the probability in the full domain. a) Training set: 0: Nothing in hand, 12493 instances (49.95202% / 50.117739%)
   1: One pair, 10599 instances, (42.37905% / 42.256903%) 2: Two pairs, 1206 instances, (4.82207% / 4.753902%) 3: Three of a kind, 513 instances, (2.05118% / 2.112845%) 4: Straight, 93 instances, (0.37185% / 0.392465%) 5: Flush, 54 instances, (0.21591% / 0.19654%) 6: Full house, 36 instances, (0.14394% / 0.144058%) 7: Four of a kind, 6 instances, (0.02399% / 0.02401%) 8: Straight flush, 5 instances, (0.01999% / 0.001385%) 9: Royal flush, 5 instances, (0.01999% / 0.000154%)
   The Straight flush and Royal flush hands are not as representative of the true domain because they have been over-sampled. The Straight flush is 14.43 times more likely to occur in the training set, while the Royal flush is 129.82 times more likely. Total of 25010 instances in a domain of 311,875,200. b) Testing set: The value inside parenthesis indicates the representation within the test set as compared to the entire domain. 1.0 would be perfect representation, while <1.0 are under-represented and >1.0 are over-represented.
   0: Nothing in hand, 501209 instances,(1.000063)
   1: One pair, 422498 instances,(0.999832) 2: Two pairs, 47622 instances, (1.001746) 3: Three of a kind, 21121 instances, (0.999647) 4: Straight, 3885 instances, (0.989897) 5: Flush, 1996 instances, (1.015569) 6: Full house, 1424 instances, (0.988491) 7: Four of a kind, 230 instances, (0.957934) 8: Straight flush, 12 instances, (0.866426) 9: Royal flush, 3 instances, (1.948052) Total of one million instances in a domain of 311,875,200.

7. Statistics

The number of combinations represents the number of instances in the entire domain.

src
|__ main.py
|__ preprocessor.py
|__ utils.py
stream.sh

• Install spark in your machine and make sure spark-submit works fine. Found this to be really useful resource to install spark in mac.
• Open two terminal windows. Traverse to /src for running the driver program.
• The driver program is src/main.py. Run the driver program in one of the terminals with the following command - spark-submit src/main.py <port_to_stream_data_from> <interval_for_streaming> <path_to_test_data>
• On another terminal, run ./stream.sh <path_to_poker-data.txt> <port_to_stream_data_to> <interval_for_streaming> <batch_size> <ignore_header_flag[true or false]>

Note: All commands assume that the current working directory is the project root. Port number and streaming interval should be the same in both the commands.