ledmands commited on
Commit
47aa47a
1 Parent(s): 91b9fbd

Removed unecessary comments in plot_improvement.py

Browse files
Files changed (1) hide show
  1. plot_improvement.py +3 -9
plot_improvement.py CHANGED
@@ -1,10 +1,9 @@
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- import argparse
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  import numpy as np
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  import os
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  from matplotlib import pyplot as plt
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  def calc_stats(filepath):
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- # load the numpy file
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  data = np.load(filepath)["results"]
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  # sort the arrays and delete the first and last elements
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  data = np.sort(data, axis=1)
@@ -19,11 +18,6 @@ def calc_stats(filepath):
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  # parser.add_argument("-s", "--save", help="Specify whether to save the chart.", action="store_const", const=True)
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  # args = parser.parse_args()
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- # Get the file paths and store in list.
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- # For each file path, I want to calculate the mean reward. This would be the mean reward for the training run over all evaluations.
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- # For each file path, append the mean reward to an averages list
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- # Plot the averages!
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-
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  filepaths = []
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  for d in os.listdir("agents/"):
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  if "dqn_v2" in d:
@@ -40,8 +34,8 @@ for path in filepaths:
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  runs = []
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  for i in range(len(filepaths)):
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  runs.append(i + 1)
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- plt.xlabel("training runs")
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- plt.ylabel("score")
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  plt.bar(runs, means)
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  plt.bar(runs, stds)
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  plt.legend(["Mean evaluation score", "Standard deviation"])
 
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+ # import argparse
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  import numpy as np
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  import os
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  from matplotlib import pyplot as plt
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  def calc_stats(filepath):
 
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  data = np.load(filepath)["results"]
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  # sort the arrays and delete the first and last elements
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  data = np.sort(data, axis=1)
 
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  # parser.add_argument("-s", "--save", help="Specify whether to save the chart.", action="store_const", const=True)
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  # args = parser.parse_args()
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  filepaths = []
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  for d in os.listdir("agents/"):
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  if "dqn_v2" in d:
 
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  runs = []
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  for i in range(len(filepaths)):
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  runs.append(i + 1)
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+ plt.xlabel("Training Run")
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+ plt.ylabel("Score")
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  plt.bar(runs, means)
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  plt.bar(runs, stds)
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  plt.legend(["Mean evaluation score", "Standard deviation"])