keras models ready for testing

hypotheses_modeling/KerasRegressions.py

@@ -86,7 +86,7 @@ def time_series_dnn_classification(X, Y, k, n0, x_columns, y_columns):
   y = np.array(y)
   model = tf.keras.Sequential([
     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)
   ])
   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)
   print(loss, accuracy)
   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()