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Building the database using sql file

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Paul
2025-04-02 15:11:46 -04:00
parent fa870f3f54
commit c78aa38db9
8 changed files with 642 additions and 34 deletions
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2
.gitignore vendored
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.Trash-1000
target
database

13
Pipfile Normal file
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[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
numpy = "*"
matplotlib = "*"
[dev-packages]
[requires]
python_version = "3.11"

441
Pipfile.lock generated Normal file
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{
"_meta": {
"hash": {
"sha256": "e540abdff28d409846f750f9fedb2971e542d6c15b86b99496a68bd9839717bc"
},
"pipfile-spec": 6,
"requires": {
"python_version": "3.11"
},
"sources": [
{
"name": "pypi",
"url": "https://pypi.org/simple",
"verify_ssl": true
}
]
},
"default": {
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"sha256:e63e4e5081de46517099dc30abe418122f54531a6ae2ebc8680bcd7096860eab",
"sha256:f189805c8be5ca5add39e6f899e6ce2ed824e65fb45f3c28cb2841911da19070",
"sha256:f7955ecf5609dee9442cbface754f2c6e541d9e6eda87fad7f7a989b0bdb9d71",
"sha256:f86d3a7a9af5d826744fabf4afd15b9dfef44fe69a98541f666f66fbb8d3fef9",
"sha256:fbd43429d0d7ed6533b25fc993861b8fd512c42d04514a0dd6337fb3ccf22761"
],
"markers": "python_version >= '3.9'",
"version": "==11.1.0"
},
"pyparsing": {
"hashes": [
"sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf",
"sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be"
],
"markers": "python_version >= '3.9'",
"version": "==3.2.3"
},
"python-dateutil": {
"hashes": [
"sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3",
"sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"
],
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'",
"version": "==2.9.0.post0"
},
"six": {
"hashes": [
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"sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"
],
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'",
"version": "==1.17.0"
}
},
"develop": {}
}

148
data/build_db.sql Normal file
View File

@@ -0,0 +1,148 @@
CREATE TABLE IF NOT EXISTS teams (
team CHAR(3) NOT NULL,
park_id CHAR(5) NOT NULL,
league CHAR(2) NOT NULL,
FOREIGN KEY (park_id) REFERENCES parks(park_id),
PRIMARY KEY (team)
);
CREATE TABLE IF NOT EXISTS games (
id INTEGER NOT NULL,
game_date date NOT NULL,
game_number TINYINT UNSIGNED NOT NULL,
day_of_week TINYINT UNSIGNED NOT NULL,
length_in_outs TINYINT UNSIGNED NOT NULL,
day_night BIT NOT NULL,
completion_info VARCHAR,
forfeit CHAR(1),
protest CHAR(1),
park_id CHAR(5) NOT NULL,
attendence INT UNSIGNED NOT NULL,
length_in_minutes INT UNSIGNED NOT NULL,
home_plate_ump_id CHAR(8),
home_plate_ump_name VARCHAR,
b1_ump_id CHAR(8),
b1_ump_name VARCHAR,
b2_ump_id CHAR(8),
b2 ump_name VARCHAR,
b3_ump_id CHAR(8),
b3_ump_name VARCHAR,
lf_ump_id CHAR(8),
lf_ump_name VARCHAR,
rf_ump_id CHAR(8),
rf_ump_name VARCHAR,
weather_id INT NOT NULL,
FOREIGN KEY (weather_id) REFERENCES weather(id)
PRIMARY KEY (id)
);
CREATE TABLE IF NOT EXISTS parks (
park_id CHAR(5) NOT NULL,
park_name VARCHAR,
latitude FLOAT,
longitude FLOAT,
FOREIGN KEY (park_id) REFERENCES games(park_id),
PRIMARY KEY (park_id)
);
CREATE TABLE IF NOT EXISTS weather (
id INTEGER NOT NULL,
temperature SMALLINT,
wind_speed FLOAT,
air_pressure FLOAT,
humidity SMALLINT UNSIGNED,
uv_index FLOAT,
air_quality TINYINT UNSIGNED,
percipitation_type CHAR(10),
percipitation_amount FLOAT,
sky_condition CHAR(20),
sun_rise TIME,
sun_set TIME,
moon_phase TINYINT UNSIGNED,
PRIMARY KEY (id)
);
CREATE TABLE IF NOT EXISTS team_game (
game INTEGER NOT NULL,
team CHAR(3) NOT NULL,
game_num TINYINT UNSIGNED,
score TINYINT UNSIGNED,
line_score CHAR(9),
at_bats TINYINT UNSIGNED,
hits TINYINT UNSIGNED,
doubles TINYINT UNSIGNED,
triples TINYINT UNSIGNED,
homeruns TINYINT UNSIGNED,
rbis TINYINT UNSIGNED,
sacrifice_hits TINYINT UNSIGNED,
sacrifice_flies TINYINT UNSIGNED,
hit_by_pitch TINYINT UNSIGNED,
walks TINYINT UNSIGNED,
intentional_walks TINYINT UNSIGNED,
strikeouts TINYINT UNSIGNED,
stolen_bases TINYINT UNSIGNED,
caught_stealing TINYINT UNSIGNED,
grounded_double TINYINT UNSIGNED,
interference TINYINT UNSIGNED,
left_on_base TINYINT UNSIGNED,
pitchers_used TINYINT UNSIGNED,
individual_earned_runs TINYINT UNSIGNED,
earned_runs TINYINT UNSIGNED,
wild_pitches TINYINT UNSIGNED,
balks TINYINT UNSIGNED,
putouts TINYINT UNSIGNED,
assists TINYINT UNSIGNED,
errors TINYINT UNSIGNED,
passed TINYINT UNSIGNED,
double_play TINYINT UNSIGNED,
triple_play TINYINT UNSIGNED,
manager_id CHAR(8),
manager_name VARCHAR,
starting_pitchter_id CHAR(8),
starting_pitcher_name VARCHAR,
starting_1_id CHAR(8),
starting_1_name VARCHAR,
starting_1_position TINYINT UNSIGNED,
starting_2_id CHAR(8),
starting_2_name VARCHAR,
starting_2_position TINYINT UNSIGNED,
starting_3_id CHAR(8),
starting_3_name VARCHAR,
starting_3_position TINYINT UNSIGNED,
starting_4_id CHAR(8),
starting_4_name VARCHAR,
starting_4_position TINYINT UNSIGNED,
starting_5_id CHAR(8),
starting_5_name VARCHAR,
starting_5_position TINYINT UNSIGNED,
starting_6_id CHAR(8),
starting_6_name VARCHAR,
starting_6_position TINYINT UNSIGNED,
starting_7_id CHAR(8),
starting_7_name VARCHAR,
starting_7_position TINYINT UNSIGNED,
starting_8_id CHAR(8),
starting_8_name VARCHAR,
starting_8_position TINYINT UNSIGNED,
starting_9_id CHAR(8),
starting_9_name VARCHAR,
starting_9_position TINYINT UNSIGNED,
FOREIGN KEY (game) REFERENCES games(id),
FOREIGN KEY (team) REFERENCES teams(team)
PRIMARY KEY (game, team)
);

9
data/db_connect.py
View File

@@ -4,6 +4,15 @@ class Database:
def __init__(self, database_file):
self.db = sqlite3.connect(database_file)
def build_database(self, sql_file_path):
sql_script_string = ''
with open(sql_file_path, 'r') as sql_file:
sql_script_string = sql_file.read()
cursor = self.db.cursor()
cursor.executescript(sql_script_string)
self.db.commit()
def select(self, index):
# Query the database for the specified index
cursor = self.db.cursor()

1
data/stats/gl2022.csv
View File

@@ -1,3 +1,4 @@
"date","num-of-game","day-of-week","visiting-team","visiting-league","visiting-game-num","home-team","home-league","home-game-num","visiting-score","home-score","length-in-outs","day-night","completion-info","forfeit","protest","park-id","attendance","length-in-min","visiting-line-scores","home-line-scores","visiting-at-bats","visiting-hits","visiting-doubles","visiting-triples","visiting-homeruns","visiting-rbi","visiting-sacrifice-hits","visiting-sacrifice-flies","visiting-hit-by-pitch","visiting-walks","visiting-intentional-walks","visiting-strikeouts","visiting-stolen-bases","visiting-caught-stealing","visiting-grounded-double","visiting-interference","visiting-left-on-base","visiting-pitchers-used","visiting-individual-earned-runs","visiting-team-earned-runs","visiting-wild-pitches","visiting-balks","visiting-putouts","visiting-assists","visiting-errors","visiting-passed","visiting-double-play","visiting-triple-play","home-at-bats","home-hits","home-doubles","home-triples","home-homeruns","home-rbi","home-sacrifice-hits","home-sacrifice-flies","home-hit-by-pitch","home-walks","home-intentional-walks","home-strikeouts","home-stolen-bases","home-caught-stealing","home-grounded-double","home-interference","home-left-on-base","home-pitchers-used","home-individual-earned-runs","home-team-earned-runs","home-wild-pitches","home-balks","home-putouts","home-assists","home-errors","home-passed","home-double-play","home-triple-play","home-plate-ump-id",home-plate-ump-name","1b-plate-ump-id","1b-plate-ump-name","2b-plate-ump-id","2b-plate-ump-name","3b-plate-ump-id","3b-plate-ump-name","lf-plate-ump-id","lf-plate-ump-name","rf-plate-ump-id","rf-plate-ump-name","visiting-manager-id","visiting-manager-name","home-manager-id","home-manager-name","winning-pitcher-id","winning-pitcher-name","losing-pitcher-id","losing-pitcher-name","saving-pitcher-id","saving-pitcher-name","rbi-batter-id","rbi-batter-name","visiting-start-pitcher-id","visiting-start-pitcher-name","home-start-pitcher-id","home-start-pitcher-name","visiting-starting-1-id","visiting-starting-1-name","visiting-starting-1-position","visiting-starting-2-id","visiting-starting-2-name","visiting-starting-2-position","visiting-starting-3-id","visiting-starting-3-name","visiting-starting-3-position","visiting-starting-4-id","visiting-starting-4-name","visiting-starting-4-position","visiting-starting-5-id","visiting-starting-5-name","visiting-starting-5-position","visiting-starting-6-id","visiting-starting-6-name","visiting-starting-6-position","visiting-starting-7-id","visiting-starting-7-name","visiting-starting-7-position","visiting-starting-8-id","visiting-starting-8-name","visiting-starting-8-position","visiting-starting-9-id","visiting-starting-9-name","visiting-starting-9-position","home-starting-1-id","home-starting-1-name","home-starting-1-position","home-starting-2-id","home-starting-2-name","home-starting-2-position","home-starting-3-id","home-starting-3-name","home-starting-3-position","home-starting-4-id","home-starting-4-name","home-starting-4-position","home-starting-5-id","home-starting-5-name","home-starting-5-position","home-starting-6-id","home-starting-6-name","home-starting-6-position","home-starting-7-id","home-starting-7-name","home-starting-7-position","home-starting-8-id","home-starting-8-name","home-starting-8-position","home-starting-9-id","home-starting-9-name","home-starting-9-position","additional-info","aquisition-info"
"20220407","0","Thu","SDN","NL",1,"ARI","NL",1,2,4,51,"N","","","","PHO01",35508,198,"001100000","000000004",29,4,1,0,0,2,0,0,0,7,0,5,1,0,2,0,7,5,4,4,2,0,24,8,0,0,1,0,26,3,0,0,1,3,0,0,1,7,0,6,0,0,1,0,6,6,2,2,0,0,27,9,0,0,2,0,"vanol901","Larry Vanover","belld901","Dan Bellino","barbs901","Sean Barber","valej901","Junior Valentine","","(none)","","(none)","melvb001","Bob Melvin","lovut001","Tony Lovullo","mantj002","Joe Mantiply","suarr002","Robert Suarez","","(none)","beers001","Seth Beer","darvy001","Yu Darvish","bumgm001","Madison Bumgarner","nolaa002","Austin Nola",2,"machm001","Manny Machado",5,"cronj001","Jake Cronenworth",4,"voitl001","Luke Voit",10,"myerw001","Wil Myers",9,"hosme001","Eric Hosmer",3,"profj001","Jurickson Profar",7,"kim-h002","Ha-Seong Kim",6,"grist001","Trent Grisham",8,"varsd001","Daulton Varsho",8,"martk001","Ketel Marte",4,"perad001","David Peralta",7,"walkc002","Christian Walker",3,"smitp002","Pavin Smith",9,"kellc002","Carson Kelly",2,"beers001","Seth Beer",10,"ellid002","Drew Ellis",5,"perdg001","Gerardo Perdomo",6,"","Y"
"20220407","0","Thu","CIN","NL",1,"ATL","NL",1,6,3,54,"N","","","","ATL03",40545,181,"012003000","001000020",35,10,0,0,1,6,0,1,1,1,0,13,0,0,1,0,5,5,2,2,0,0,27,7,1,0,0,0,31,4,0,0,1,2,0,0,0,5,0,13,0,0,0,0,6,4,6,6,0,0,27,7,0,0,1,0,"laynj901","Jerry Layne","wendh902","Hunter Wendelstedt","whitc901","Chad Whitson","hamaa901","Adam Hamari","","(none)","","(none)","belld002","David Bell","snitb801","Brian Snitker","mahlt001","Tyler Mahle","friem001","Max Fried","santt001","Tony Santillan","farmk001","Kyle Farmer","mahlt001","Tyler Mahle","friem001","Max Fried","indij001","Jonathan India",4,"aquia001","Aristides Aquino",9,"phamt001","Tommy Pham",7,"vottj001","Joey Votto",3,"stept001","Tyler Stephenson",2,"senzn001","Nick Senzel",8,"mousm001","Mike Moustakas",10,"farmk001","Kyle Farmer",6,"drurb001","Brandon Drury",5,"rosae001","Eddie Rosario",9,"olsom001","Matt Olson",3,"rilea001","Austin Riley",5,"ozunm001","Marcell Ozuna",7,"albio001","Ozzie Albies",4,"duvaa001","Adam Duvall",8,"darnt001","Travis d'Arnaud",2,"dicka001","Alex Dickerson",10,"swand001","Dansby Swanson",6,"","Y"
"20220407","0","Thu","MIL","NL",1,"CHN","NL",1,4,5,51,"D","","","","CHI11",35112,198,"000100210","00003020x",33,10,4,0,0,4,0,2,1,4,0,9,1,0,2,0,9,3,5,5,0,0,24,10,0,0,1,0,29,8,3,0,1,5,0,1,1,4,0,7,0,1,1,0,6,6,4,4,0,0,27,9,0,0,2,0,"barrt901","Ted Barrett","barkl901","Lance Barksdale","lentn901","Nic Lentz","cejan901","Nestor Ceja","","(none)","","(none)","counc001","Craig Counsell","rossd001","David Ross","givem001","Mychal Givens","ashba003","Aaron Ashby","robed002","David Robertson","happi001","Ian Happ","burnc002","Corbin Burnes","hendk001","Kyle Hendricks","wongk001","Kolten Wong",4,"adamw002","Willy Adames",6,"yelic001","Christian Yelich",7,"mccua001","Andrew McCutchen",10,"tellr001","Rowdy Tellez",3,"renfh001","Hunter Renfroe",9,"narvo001","Omar Narvaez",2,"cainl001","Lorenzo Cain",8,"petej002","Jace Peterson",5,"orter001","Rafael Ortega",10,"madrn001","Nick Madrigal",4,"contw001","Willson Contreras",2,"happi001","Ian Happ",7,"schwf001","Frank Schwindel",3,"suzus001","Seiya Suzuki",9,"heywj001","Jason Heyward",8,"wisdp001","Patrick Wisdom",5,"hoern001","Nico Hoerner",6,"","Y"
Can't render this file because it is too large.

21
data/stats_importer.py
View File

@@ -7,22 +7,7 @@ def parse_csv(file_path):
# Open and read the CSV file
with open(file_path, 'r') as csvfile:
# Read each row using csv.reader
reader = csv.reader(ciphertextcsvfile)
reader = csv.DictReader(ciphertextcsvfile)
list_of_dict = list(reader)
# Initialize an empty list to store the result
result = []
# Iterate over each row in the CSV
for row in reader:
# Create a dictionary for the current row
person_dict = {
'name': row[0],
'address': row[1],
'number_of_kids': int(row[2]),
'night_or_day': row[3]
}
# Append the dictionary to the result list
result.append(person_dict)
return result
return list_of_dict

41
main.py
View File

@@ -1,5 +1,6 @@
import numpy as np # helps with the math
import matplotlib.pyplot as plt # to plot error during training
from data.db_connect import Database
# input data
inputs = np.array([[0, 0, 1, 0],
@@ -55,22 +56,30 @@ class NeuralNetwork:
prediction = self.sigmoid(np.dot(new_input, self.weights))
return prediction
# create neural network
NN = NeuralNetwork(inputs, outputs)
# train neural network
NN.train()
if __name__ == '__main__':
sql_file = "./data/build_db.sql"
db_file = "./database/baseball.db"
# create two new examples to predict
example = np.array([[1, 1, 1, 0]])
example_2 = np.array([[0, 0, 1, 1]])
db_conn = Database(db_file)
db_conn.build_database(sql_file)
# print the predictions for both examples
print(NN.predict(example), ' - Correct: ', example[0][0])
print(NN.predict(example_2), ' - Correct: ', example_2[0][0])
else:
# create neural network
NN = NeuralNetwork(inputs, outputs)
# train neural network
NN.train()
# plot the error over the entire training duration
plt.figure(figsize=(15,5))
plt.plot(NN.epoch_list, NN.error_history)
plt.xlabel('Epoch')
plt.ylabel('Error')
plt.savefig('plot.png')
# create two new examples to predict
example = np.array([[1, 1, 1, 0]])
example_2 = np.array([[0, 0, 1, 1]])
# print the predictions for both examples
print(NN.predict(example), ' - Correct: ', example[0][0])
print(NN.predict(example_2), ' - Correct: ', example_2[0][0])
# plot the error over the entire training duration
plt.figure(figsize=(15,5))
plt.plot(NN.epoch_list, NN.error_history)
plt.xlabel('Epoch')
plt.ylabel('Error')
plt.savefig('plot.png')