From e6b8a9f2132110c3df19f8bff2042403d91cbd28 Mon Sep 17 00:00:00 2001
From: paul <paul@halvo.me>
Date: Fri, 19 Sep 2025 21:50:46 +0000
Subject: [PATCH] Initial setup for training

---
 Pipfile                    |   2 +
 Pipfile.lock               | 983 +++++++++++++++++++++++++++++--------
 data/get_data.py           |  38 +-
 main.py                    |  56 ++-
 neuralnet/neuralnetwork.py |  44 +-
 5 files changed, 901 insertions(+), 222 deletions(-)

diff --git a/Pipfile b/Pipfile
index 230e135..fd02efe 100644
--- a/Pipfile
+++ b/Pipfile
@@ -8,8 +8,10 @@ ephem = "*"
 matplotlib = "*"
 numpy = "*"
 requests = "*"
+rich = "*"
 suntime = "*"
 timezonefinder = "*"
+tensorflow = "*"
 
 [dev-packages]
 
diff --git a/Pipfile.lock b/Pipfile.lock
index 13a4ac2..79b4e45 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "7ad75fcb917b3a288fd1156f75a1f0ece49414a473d45695e594becbd9eb1057"
+            "sha256": "759185813a6ecf980849429c234b628048f6c38190da94a77b0dcab8e9f0331a"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -16,6 +16,21 @@
         ]
     },
     "default": {
+        "absl-py": {
+            "hashes": [
+                "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9",
+                "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==2.3.1"
+        },
+        "astunparse": {
+            "hashes": [
+                "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872",
+                "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"
+            ],
+            "version": "==1.6.3"
+        },
         "certifi": {
             "hashes": [
                 "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407",
@@ -383,67 +398,140 @@
         },
         "fonttools": {
             "hashes": [
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@@ -877,21 +1264,44 @@
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@@ -910,6 +1320,23 @@
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@@ -926,6 +1353,57 @@
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+                "sha256:af338aa93554be859173c39c85243970dc6a289fa907402289eeae7543e1ae18",
+                "sha256:afd964fd43b10c12213574db492cb8f73b2f0826c8df07a68288f8f19af2ebe6",
+                "sha256:b32888aad8b6e68f83a8fdccbf3165f5469702a7544472bdf41f582970ed3311",
+                "sha256:c31eebe420a9a5d2887b13000b043ff6ca27c452a9a22fa71f35f118e8d4bf89",
+                "sha256:caea3e9c79d5f0d2c6d9ab96111601797ea5da8e6d0723f77eabb0d4068d2b2f",
+                "sha256:cf30f6e3c077c8e6a9a7809c94551203c8843e74ba0c960f4a98cd80d4665d39",
+                "sha256:d40770d7c0fd5cbed9d84b2c3f2e156431a12c9a37dc6284060fb4bec0b7ffd4",
+                "sha256:d8a210b158a34164de8bb68b0e7780041a903d7b00c87e906fb69928bf7890d5",
+                "sha256:dc4a8d2b25efb6681ecacad42fca8859f88092d8732b170de6a5dddd80a1c8fa",
+                "sha256:df7d30371a2accfe4013e90445f6388c570f103d61019b6b7c57e0265250072a",
+                "sha256:e01375f275f010fcbf7f643b4279896d04e571889b8a5b3f848423d91bf07050",
+                "sha256:e1a4120ae5705f673727d3253de3ed0e016f7cd78dc463db1b31e2463e1f3cf6",
+                "sha256:e228514a06843cae89621384cfe3a80418f3c04aadf8a3b14e46a7be704e4235",
+                "sha256:e405adefb53a435f01efa7ccdec012c016b5a1d3f35459990afc39b6be4d5056",
+                "sha256:e6b13af258d6a9ad602d57d889f83b9d5543acd471eee12eb51f5b01f8eb1bc2",
+                "sha256:e6f40a8aa5a92f150bdb3e1c44b7e98fb7113955b2e5394122fa5532fec4b418",
+                "sha256:e71d5c6ebac14875668a1e90baf2ea0ef5b7ac7918355850c0908ae82bcb297c",
+                "sha256:ed7c635ae45cfbc1a7371f708727bf74690daedc49b4dba310590ca0bd28aa8a",
+                "sha256:f38e60678850c42461d4202739f9bf1e3a737c7ad283638251e79cc49effb6b6",
+                "sha256:f66eb08feaa410fe4eebd17f2a2c8e2e46d3476e9f8c783daa8e09e0faa666d0",
+                "sha256:f9b2601381be482f70e5d1051a5965c25fb3625455a2bf520b5a077b22afb775",
+                "sha256:fbd3c8319de8e1dc79d346929cd71d523622da527cca14e0c1d257e31c2b8b10",
+                "sha256:fd341868a4b6714a5962c1af0bd44f7c404ef78720c7de4892901e540417111c"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==1.17.3"
         }
     },
     "develop": {}
diff --git a/data/get_data.py b/data/get_data.py
index 376ece7..9d3c58e 100644
--- a/data/get_data.py
+++ b/data/get_data.py
@@ -1,5 +1,8 @@
 from data.db_connect import Database
 
+import hashlib
+import numpy as np
+
 # Game date in YYYYmmDD
 def pull_training_data(database: Database, game_date: str, game_number: int, park_id: str):
     # Training data
@@ -278,7 +281,7 @@ def pull_training_data(database: Database, game_date: str, game_number: int, par
         print(f"Got the wrong number of games {len(curr_team_game)}")
         return None
 
-    training_result = (curr_team_game[0][0] == 1 and curr_team_game[0][2] == 1)
+    training_result = int((curr_team_game[0][0] == 1 and curr_team_game[0][2] == 1))
 
     select_previous_games = """
         SELECT
@@ -349,10 +352,10 @@ def pull_training_data(database: Database, game_date: str, game_number: int, par
     select_previous_games_data_1 = [first_of_the_year, game_date, curr_team_game[1][1]]
 
     training_data = [*curr_game[1:]]
-
     training_data = [*training_data, *curr_team_game[0][1:]]
+
     prev_games = database.selectall(select_previous_games, select_previous_games_data_0)
-    prev_game_data = [0] * 61
+    prev_game_data = [0] * 85
     if prev_games is not None:
         prev_win_streak = 0
         index = len(prev_games) - 1
@@ -372,11 +375,12 @@ def pull_training_data(database: Database, game_date: str, game_number: int, par
             prev_loss_streak,
             *prev_games[index][1:]
         ]
+
     training_data = [*training_data, *prev_game_data]
-
     training_data = [*training_data, *curr_team_game[1][1:]]
+
     prev_games = database.selectall(select_previous_games, select_previous_games_data_1)
-    prev_game_data = [0] * 61
+    prev_game_data = [0] * 85
     if prev_games is not None:
         prev_win_streak = 0
         index = len(prev_games) - 1
@@ -396,6 +400,30 @@ def pull_training_data(database: Database, game_date: str, game_number: int, par
             prev_loss_streak,
             *prev_games[index][1:]
         ]
+
     training_data = [*training_data, *prev_game_data]
 
     return (training_result, training_data)
+
+def convert_to_integer(value):
+    if isinstance(value, int):
+        return value
+    elif isinstance(value, str):
+        return int(
+            hashlib.blake2b(
+                data=value.encode(),
+                digest_size=3,
+            ).hexdigest(),
+            16
+        )
+    elif isinstance(value, float):
+        # Times 100 should be unique enough
+        return int(value * 100)
+    elif isinstance(value, bool):
+        return int(value)
+    else:
+        return -1000
+
+def normalize_training_data(data: list):
+    new_data = np.array([convert_to_integer(val) for val in data], dtype=np.int64)
+    return new_data
\ No newline at end of file
diff --git a/main.py b/main.py
index 87ebfb5..b0d980c 100644
--- a/main.py
+++ b/main.py
@@ -1,11 +1,16 @@
 import numpy as np # helps with the math
 import matplotlib.pyplot as plt # to plot error during training
+import tensorflow as tf
 
-from data.get_data import pull_training_data
+from rich.progress import Progress
+
+from data.get_data import pull_training_data, normalize_training_data
 from data.db_connect import Database
 from data.build_weather import get_weather, get_sun_and_moon_phase
 from data.stats_importer import Importer
 
+from neuralnet.neuralnetwork import NeuralNetwork
+
 # input data
 inputs = np.array([[0, 0, 1, 0],
                    [0, 0, 1, 1],
@@ -23,13 +28,54 @@ if __name__ == '__main__':
     query = "SELECT game_date, game_number, park_id FROM games ORDER BY game_date"
     all_games = db_conn.selectall(query)
 
-    for game in all_games:
-        game_result, training_data = pull_training_data(db_conn, str(game[0]), game[1], game[2])
-
+    compiled_training_data = None
+    compiled_training_results = None
     
+    with Progress() as p_bar:
+        p_bar_val = p_bar.add_task("Getting Data ...", total=len(all_games))
+        for game in all_games:
+            p_bar.update(p_bar_val, advance=1)
+            game_result, training_data = pull_training_data(db_conn, str(game[0]), game[1], game[2])
+
+            training_data = normalize_training_data(training_data)
+
+            if compiled_training_results is None:
+                compiled_training_data = np.array([training_data])
+                compiled_training_results = np.array([game_result])
+            else:
+                compiled_training_data = np.append(compiled_training_data, np.array([training_data]), axis=0)
+                compiled_training_results = np.append(compiled_training_results, np.array([game_result]), axis=0)
+
+    ctd_len = len(compiled_training_data)
+    ctr_len = len(compiled_training_results)
+
+    td = compiled_training_data[:ctd_len-100]
+    tdt = compiled_training_data[ctd_len-100:]
+
+    tr = compiled_training_results[:ctr_len-100]
+    trt = compiled_training_results[ctr_len-100:]
+
+    nn = NeuralNetwork(len(compiled_training_data[0]))
+    nn.train(td, tr)
+    nn.summary()
+    predictions = nn.predict(np.array(tdt))
+
+    total_num = 0
+    accurate_num = 0
+    for pred, act in zip(predictions, trt):
+        total_num += 1
+        pred = round(pred[0] * 1000) / 1000.0
+        guess = round(pred)
+        print(f"Pred: {pred} -> Gue: {guess} -> Res: {act} -> Cor: {guess == act}")
+
+        accurate_num += int(guess==act)
+
+    print(f"Total: {total_num} -> Accu: {accurate_num} -> Perc: {accurate_num/total_num}")
+    #for index in range(len(trt)):
+    #    print(f"pred: {nn.predict(np.array([tdt[index]]))} : act : {trt[index]}")
 
     """
-    build_db_path = "./data/sql/build_db.sql"
+    build_db_path = "./data/sql/build_db .sql"
     fill_parks_path = "./data/sql/prefill_parks.sql"
     fill_teams_path = "./data/sql/prefill_teams.sql"
 
diff --git a/neuralnet/neuralnetwork.py b/neuralnet/neuralnetwork.py
index 7b6c502..018c73c 100644
--- a/neuralnet/neuralnetwork.py
+++ b/neuralnet/neuralnetwork.py
@@ -1,26 +1,40 @@
+import os
 import numpy as np
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense
+import tensorflow as tf
 
 # create NeuralNetwork class
 class NeuralNetwork:
-    def __init__(self, inputs_len: int):
+    def __init__(self, input_length: int):
+        self.model = tf.keras.Sequential([
+            tf.keras.Input(shape=(input_length,), dtype=tf.int64),
+            tf.keras.layers.Dense(512, activation='relu'),
+            tf.keras.layers.Dropout(0.5),
+            tf.keras.layers.Dense(256, activation='relu'),
+            tf.keras.layers.Dropout(0.5),
+            tf.keras.layers.Dense(128, activation='relu'),
+            tf.keras.layers.Dense(1, activation='sigmoid'),
+        ])
+
+        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
         # Setup checkpoint
-        self.checkpoint_path = "./training/cp.ckpt"
-        self.cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1)
+        self.checkpoint_path = "./training/cp.ckpt.weights.h5"
+        self.cp_callback = tf.keras.callbacks.ModelCheckpoint(
+            filepath=self.checkpoint_path, 
+            save_weights_only=True, 
+            save_best_only=True,
+            monitor='loss',
+            mode='min',
+        )
 
-        # Setup model
-        self.model = Sequential()
-        self.model.add(Dense(12, input_shape=(inputs_len,), activation='relu'))
-        self.model.add(Dense(8, activation='relu'))
-        self.model.add(Dense(1, activation='sigmoid'))
-        self.model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+        #if os.path.isfile(self.checkpoint_path):
+        #    self.model.load_weights(self.checkpoint_path)
 
-        if os.path.isfile(self.checkpoint_path):
-            self.model.load_weights(self.checkpoint_path)
+    def train(self, inputs :list, outputs :list):
+        self.model.fit(inputs, outputs, epochs=100, batch_size=64, callbacks=[self.cp_callback])
 
-    def train(inputs :list, outputs :list):
-        self.model.fit(inputs, outputs, epochs=150, batch_size=10, callbacks=[self.cp_callback])
+    def summary(self):
+        print(self.model.summary())
 
     def predict(self, new_input):
         return self.model.predict(new_input)
\ No newline at end of file