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218 lines
6.5 KiB
218 lines
6.5 KiB
import tensorflow as tf
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import pandas as pd
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import numpy as np
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from sklearn.metrics import r2_score
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def r2_(y, pred):
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ybar = np.sum(y) / len(y)
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ssreg = np.sum((pred - ybar)**2)
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sstot = np.sum((y - ybar)**2)
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return ssreg/sstot
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def time_series_sigmoid_classification(dataset, k, n0, x_columns, y_columns):
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inp = dataset[x_columns]
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out = dataset[y_columns]
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col = "day"
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x = []
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y = []
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input_shape = 0
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output_shape = 0
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for player in out["playerID"].unique():
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XPlayer = inp[inp["playerID"] == player]
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YPlayer = out[out["playerID"] == player]
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for day in YPlayer[col][n0 - 1:]:
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prev = day - k
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xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy()
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if xprev.shape[0] != 1:
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continue
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else:
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xprev = xprev[0, :]
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yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).to_numpy()[0, :]
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if input_shape == 0:
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input_shape = xprev.shape[0]
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else:
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if input_shape != xprev.shape[0]:
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print("INCONSISTENT INPUT DIMENSION")
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exit(2)
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if output_shape == 0:
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output_shape = yt.shape[0]
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else:
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if output_shape != yt.shape[0]:
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print("INCONSISTENT OUTPUT DIMENSION")
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exit(2)
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x.append(xprev)
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y.append(yt)
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x = np.array(x)
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y = np.array(y)
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model = tf.keras.Sequential([
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tf.keras.layers.Flatten(input_shape=[input_shape]),
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tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax)
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])
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
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model.fit(x, y, epochs=50)
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loss, accuracy = model.evaluate(x, y)
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print(loss, accuracy)
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return model.get_weights()
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def time_series_dnn_classification(dataset, k, n0, x_columns, y_columns):
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inp = dataset[x_columns]
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out = dataset[y_columns]
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col = "day"
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x = []
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y = []
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input_shape = 0
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output_shape = 0
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for player in out["playerID"].unique():
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XPlayer = inp[inp["playerID"] == player]
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YPlayer = out[out["playerID"] == player]
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for day in YPlayer[col][n0 - 1:]:
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prev = day - k
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xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy()
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if xprev.shape[0] != 1:
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continue
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else:
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xprev = xprev[0, :]
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yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).to_numpy()[0, :]
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if input_shape == 0:
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input_shape = xprev.shape[0]
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else:
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if input_shape != xprev.shape[0]:
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print("INCONSISTENT INPUT DIMENSION")
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exit(2)
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if output_shape == 0:
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output_shape = yt.shape[0]
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else:
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if output_shape != yt.shape[0]:
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print("INCONSISTENT OUTPUT DIMENSION")
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exit(2)
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x.append(xprev)
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y.append(yt)
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x = np.array(x)
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y = np.array(y)
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(32, input_dim=input_shape,activation=tf.nn.softmax),
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tf.keras.layers.Dense(output_shape, input_dim=32, activation=tf.nn.softmax)
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])
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
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print(output_shape)
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model.fit(x, y, epochs=50)
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loss, accuracy = model.evaluate(x, y)
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print(x.shape)
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print(y.shape)
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print(loss, accuracy)
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return model.get_weights()
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def time_series_linear_regression(dataset, k, n0, x_columns, y_columns):
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inp = dataset[x_columns]
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out = dataset[y_columns]
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col = "day"
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x = []
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y = []
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input_shape = 0
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output_shape = 0
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for player in out["playerID"].unique():
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XPlayer = inp[inp["playerID"] == player]
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YPlayer = out[out["playerID"] == player]
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for day in YPlayer[col][n0 - 1:]:
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prev = day - k
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xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy()
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if xprev.shape[0] != 1:
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continue
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else:
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xprev = xprev[0, :]
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yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).to_numpy()[0, :]
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if input_shape == 0:
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input_shape = xprev.shape[0]
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else:
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if input_shape != xprev.shape[0]:
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print("INCONSISTENT INPUT DIMENSION")
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exit(2)
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if output_shape == 0:
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output_shape = yt.shape[0]
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else:
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if output_shape != yt.shape[0]:
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print("INCONSISTENT OUTPUT DIMENSION")
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exit(2)
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x.append(xprev)
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y.append(yt)
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x = np.array(x)
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y = np.array(y)
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model = tf.keras.Sequential([
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tf.keras.layers.Flatten(input_shape=[input_shape]),
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tf.keras.layers.Dense(output_shape)
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])
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model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
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model.fit(x, y, epochs=50)
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loss, _ = model.evaluate(x, y)
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print(loss)
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pred = model.predict(x)
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r2 = r2_(y, pred)
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print(r2)
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return model.get_weights()
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def time_series_dnn_regressions(dataset, k, n0, x_columns, y_columns):
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inp = dataset[x_columns]
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out = dataset[y_columns]
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col = "day"
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x = []
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y = []
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input_shape = 0
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output_shape = 0
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for player in out["playerID"].unique():
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XPlayer = inp[inp["playerID"] == player]
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YPlayer = out[out["playerID"] == player]
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for day in YPlayer[col][n0 - 1:]:
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prev = day - k
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xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy()
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if xprev.shape[0] != 1:
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continue
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else:
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xprev = xprev[0, :]
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yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).to_numpy()[0, :]
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if input_shape == 0:
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input_shape = xprev.shape[0]
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else:
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if input_shape != xprev.shape[0]:
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print("INCONSISTENT INPUT DIMENSION")
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exit(2)
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if output_shape == 0:
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output_shape = yt.shape[0]
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else:
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if output_shape != yt.shape[0]:
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print("INCONSISTENT OUTPUT DIMENSION")
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exit(2)
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x.append(xprev)
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y.append(yt)
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x = np.array(x)
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y = np.array(y)
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model = tf.keras.Sequential([
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tf.keras.layers.Flatten(input_shape=[input_shape]),
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tf.keras.layers.Dense(32, activation=tf.nn.softmax),
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tf.keras.layers.Dense(output_shape)
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])
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model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
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model.fit(x, y, epochs=100, verbose=0)
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loss, accuracy = model.evaluate(x, y)
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print(loss, accuracy)
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pred = model.predict(x)
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r2 = r2_(y, pred)
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print(r2)
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return model.get_weights()
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def main():
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filename = "personal.csv"
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df = pd.read_csv(filename)
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x = ["day", "playerID", "DailyLoadSliding", "sleepQuality"]
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y = ["day", "playerID", "fatigueNorm"]
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k = 0
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n0 = 30
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weights = time_series_linear_regression(df, k, n0, x, y)
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if __name__ == "__main__":
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main()
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