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188 lines
5.8 KiB
188 lines
5.8 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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def time_series_sigmoid_classification(X, Y, k, n0, x_columns, y_columns):
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inp = X[x_columns]
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out = Y[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 Y["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]).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]).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 input_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=100)
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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(X, Y, k, n0, x_columns, y_columns):
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inp = X[x_columns]
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out = Y[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 Y["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]).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]).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 input_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, 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=100)
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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_linear_regression(X, Y, k, n0, x_columns, y_columns):
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inp = X[x_columns]
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out = Y[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 Y["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]).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]).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 input_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='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
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model.fit(x, y, epochs=100)
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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_regressions(X, Y, k, n0, x_columns, y_columns):
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inp = X[x_columns]
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out = Y[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 Y["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]).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]).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 input_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='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
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model.fit(x, y, epochs=100)
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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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