Initial setup for training

Pipfile

@@ -8,8 +8,10 @@ ephem = "*"
 matplotlib = "*"
 numpy = "*"
 requests = "*"
+rich = "*"
 suntime = "*"
 timezonefinder = "*"
+tensorflow = "*"
 
 [dev-packages]
 

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

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"
 

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)