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keras models ready for testing

master
PerryXDeng 5 years ago
parent
commit
2e84fa9b88
1 changed files with 93 additions and 1 deletions
  1. +93
    -1
      hypotheses_modeling/KerasRegressions.py

+ 93
- 1
hypotheses_modeling/KerasRegressions.py View File

@ -86,7 +86,7 @@ def time_series_dnn_classification(X, Y, k, n0, x_columns, y_columns):
y = np.array(y) y = np.array(y)
model = tf.keras.Sequential([ model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=input_shape), tf.keras.layers.Flatten(input_shape=input_shape),
tf.keras.layers.Dense(32),
tf.keras.layers.Dense(32, activation=tf.nn.softmax),
tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax) tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax)
]) ])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
@ -94,3 +94,95 @@ def time_series_dnn_classification(X, Y, k, n0, x_columns, y_columns):
loss, accuracy = model.evaluate(x, y) loss, accuracy = model.evaluate(x, y)
print(loss, accuracy) print(loss, accuracy)
return model.get_weights() return model.get_weights()
def time_series_linear_regression(X, Y, k, n0, x_columns, y_columns):
inp = X[x_columns]
out = Y[y_columns]
col = "day"
x = []
y = []
input_shape = 0
output_shape = 0
for player in Y["playerID"].unique():
XPlayer = inp[inp["playerID"] == player]
YPlayer = out[out["playerID"] == player]
for day in YPlayer[col][n0 - 1:]:
prev = day - k
xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy()
if xprev.shape[0] != 1:
continue
else:
xprev = xprev[0, :]
yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
if input_shape == 0:
input_shape = xprev.shape[0]
else:
if input_shape != xprev.shape[0]:
print("INCONSISTENT INPUT DIMENSION")
exit(2)
if input_shape == 0:
output_shape = yt.shape[0]
else:
if output_shape != yt.shape[0]:
print("INCONSISTENT OUTPUT DIMENSION")
exit(2)
x.append(xprev)
y.append(yt)
x = np.array(x)
y = np.array(y)
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=input_shape),
tf.keras.layers.Dense(output_shape)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
model.fit(x, y, epochs=100)
loss, accuracy = model.evaluate(x, y)
print(loss, accuracy)
return model.get_weights()
def time_series_dnn_regressions(X, Y, k, n0, x_columns, y_columns):
inp = X[x_columns]
out = Y[y_columns]
col = "day"
x = []
y = []
input_shape = 0
output_shape = 0
for player in Y["playerID"].unique():
XPlayer = inp[inp["playerID"] == player]
YPlayer = out[out["playerID"] == player]
for day in YPlayer[col][n0 - 1:]:
prev = day - k
xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy()
if xprev.shape[0] != 1:
continue
else:
xprev = xprev[0, :]
yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
if input_shape == 0:
input_shape = xprev.shape[0]
else:
if input_shape != xprev.shape[0]:
print("INCONSISTENT INPUT DIMENSION")
exit(2)
if input_shape == 0:
output_shape = yt.shape[0]
else:
if output_shape != yt.shape[0]:
print("INCONSISTENT OUTPUT DIMENSION")
exit(2)
x.append(xprev)
y.append(yt)
x = np.array(x)
y = np.array(y)
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=input_shape),
tf.keras.layers.Dense(32, activation=tf.nn.softmax),
tf.keras.layers.Dense(output_shape)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
model.fit(x, y, epochs=100)
loss, accuracy = model.evaluate(x, y)
print(loss, accuracy)
return model.get_weights()

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