From c819c339072f30cec8ce836b81f3d190e3473dfb Mon Sep 17 00:00:00 2001
From: PerryXDeng <PerryXDeng@users.noreply.github.com>
Date: Sun, 31 Mar 2019 11:33:08 -0400
Subject: [PATCH] output from multivar lin reg

---
 .~lock.DataFest 2019 - Codebook.xlsx#   |  1 +
 hypotheses_modeling/KerasRegressions.py | 28 +++++++++++++------------
 hypotheses_modeling/out.txt             |  8 +++++++
 3 files changed, 24 insertions(+), 13 deletions(-)
 create mode 100644 .~lock.DataFest 2019 - Codebook.xlsx#
 create mode 100644 hypotheses_modeling/out.txt

diff --git a/.~lock.DataFest 2019 - Codebook.xlsx# b/.~lock.DataFest 2019 - Codebook.xlsx#
new file mode 100644
index 0000000..8af3a3a
--- /dev/null
+++ b/.~lock.DataFest 2019 - Codebook.xlsx#	
@@ -0,0 +1 @@
+,pxd256,null,31.03.2019 08:16,file:///home/pxd256/.config/libreoffice/4;
\ No newline at end of file
diff --git a/hypotheses_modeling/KerasRegressions.py b/hypotheses_modeling/KerasRegressions.py
index 4ab364d..4fe6974 100644
--- a/hypotheses_modeling/KerasRegressions.py
+++ b/hypotheses_modeling/KerasRegressions.py
@@ -144,14 +144,14 @@ def time_series_linear_regression(dataset, k, n0, x_columns, y_columns):
     tf.keras.layers.Flatten(input_shape=[input_shape]),
     tf.keras.layers.Dense(output_shape)
   ])
-  model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
-  model.fit(x, y, epochs=50)
-  loss, _ = model.evaluate(x, y)
-  print(loss)
+  model.compile(optimizer='adam', loss='mean_squared_error', metrics=[tf.keras.metrics.mean_squared_error])
+  model.fit(x, y, epochs=50, verbose=2)
   pred = model.predict(x)
   r2 = r2_(y, pred)
-  print(r2)
-  return model.get_weights()
+  hard_pred = model.predict(x[0])
+  hard_out = np.matmul(x[0], [0.03589787, -0.03472298, 0.24109702, -0.10143519]) - 0.890594
+  print(hard_pred, hard_out, y[0])
+  return r2, model.get_weights()
 
 
 def time_series_dnn_regressions(dataset, k, n0, x_columns, y_columns):
@@ -194,24 +194,26 @@ def time_series_dnn_regressions(dataset, k, n0, x_columns, y_columns):
     tf.keras.layers.Dense(32, activation=tf.nn.softmax),
     tf.keras.layers.Dense(output_shape)
   ])
-  model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
-  model.fit(x, y, epochs=100, verbose=0)
+  model.compile(optimizer='adam', loss='mean_squared_error', metrics=[tf.keras.metrics.mean_squared_error])
+  model.fit(x, y, epochs=100, verbose=2)
   loss, accuracy = model.evaluate(x, y)
-  print(loss, accuracy)
   pred = model.predict(x)
   r2 = r2_(y, pred)
-  print(r2)
-  return model.get_weights()
+  return r2, model.get_weights()
 
 
 def main():
   filename = "personal.csv"
   df = pd.read_csv(filename)
-  x = ["day", "playerID", "DailyLoadSliding", "sleepQuality"]
+  x = ["day", "playerID", "sleepHoursSliding", "sleepHours", "sleepQuality", "acuteChronicRatio"]
   y = ["day", "playerID", "fatigueNorm"]
   k = 0
   n0 = 30
-  weights = time_series_linear_regression(df, k, n0, x, y)
+  r2, weights =  time_series_linear_regression(df, k, n0, x, y)
+  print("r2")
+  print(r2)
+  print("weights")
+  print(weights)
 
 
 if __name__ == "__main__":
diff --git a/hypotheses_modeling/out.txt b/hypotheses_modeling/out.txt
new file mode 100644
index 0000000..37d9d08
--- /dev/null
+++ b/hypotheses_modeling/out.txt
@@ -0,0 +1,8 @@
+Epoch 1/50, mean_squared_error: 90.4998
+Epoch 11/50, mean_squared_error: 1.0265
+Epoch 21/50, mean_squared_error: 0.9604
+Epoch 31/50, mean_squared_error: 0.8671
+Epoch 41/50, mean_squared_error: 0.7838
+r2, 0.07949744624446509
+slopes, 0.03589787,-0.03472298, 0.24109702, -0.10143519
+intercept, -0.8960594