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Sourcery Starbot ⭐ refactored radsn23/bandits-codes #1
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T = 500 | ||
N = 10 #number of batches | ||
versions = 3 #or the number of arms of the bandit | ||
l=0.1 #lambda | ||
X_size = 3 | ||
for a in alphas: | ||
T = 500 | ||
N = 10 #number of batches | ||
versions = 3 #or the number of arms of the bandit | ||
l=0.1 #lambda | ||
X_size = 3 |
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Lines 104-160
refactored with the following changes:
- Hoist statements out of for/while loops (
hoist-statement-from-loop
)
if prior == None: | ||
if prior is None: |
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Function email_mooclet.__init__
refactored with the following changes:
- Use x is None rather than x == None (
none-compare
)
tried_outputs = [0,0,0] | ||
tried_outputs = [0,0,0] |
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Lines 41-62
refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore
)
# number of trials used to compute expectation stats | ||
# set to small value when debugging for faster speed | ||
num_trials_prob_best_action = int(1e4) | ||
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if models == None: | ||
if models is None: |
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Function calculate_linucb_single_bandit
refactored with the following changes:
- Hoist statements out of for/while loops (
hoist-statement-from-loop
) - Move assignments closer to their usage (
move-assign
) - Use x is None rather than x == None (
none-compare
) - Replace index in for loop with direct reference (
for-index-replacement
) - Replace manual loop counter with call to enumerate (
convert-to-enumerate
) - Convert for loop into dictionary comprehension (
dict-comprehension
)
if len(pre_regret) == 0: | ||
if not pre_regret: |
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Function read_avg_regret
refactored with the following changes:
- Simplify sequence comparison (
simplify-len-comparison
)
pass | ||
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plt.title(title) | ||
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plt.show() | ||
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pass |
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Function plot_email_data
refactored with the following changes:
- Remove redundant pass statement (
remove-redundant-pass
)
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print(','.join(group_header), file=outf) | ||
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writer = csv.DictWriter(outf, fieldnames=field_names_out) | ||
writer.writeheader() | ||
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sample_number = 0 | ||
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cumulative_sample_regret = 0 | ||
for row in reader: | ||
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for sample_number, row in enumerate(reader): | ||
# copy the input data to output file | ||
out_row = {} | ||
out_row = {fieldname: row[fieldname] for fieldname in reader.fieldnames} | ||
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for i in range(len(reader.fieldnames)): | ||
out_row[reader.fieldnames[i]] = row[reader.fieldnames[i]] | ||
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''' write performance data (e.g. regret) for this sample''' | ||
action = chosen_actions[sample_number] | ||
observed_rewards = [int(row[HEADER_ACTUALREWARD.format(a + 1)]) for a in range(num_actions)] | ||
sampling_dist = sampling_distributions[sample_number] | ||
reward = observed_rewards[action] | ||
|
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Function writeOutFile
refactored with the following changes:
- Replace index in for loop with direct reference (
for-index-replacement
) - Replace manual loop counter with call to enumerate (
convert-to-enumerate
) - Convert for loop into dictionary comprehension (
dict-comprehension
)
@@ -76,7 +76,6 @@ def write_performance(out_row, action, optimal_action, reward, sample_regret, cu | |||
out_row[H_ALGO_SAMPLE_REGRET_CUMULATIVE] = cumulative_sample_regret | |||
out_row[H_ALGO_REGRET_EXPECTED] = expected_regret | |||
out_row[H_ALGO_REGRET_EXPECTED_CUMULATIVE] = cumulative_expected_regret | |||
pass |
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Function write_performance
refactored with the following changes:
- Remove redundant pass statement (
remove-redundant-pass
)
@@ -76,7 +76,6 @@ def write_performance(out_row, action, optimal_action, reward, sample_regret, cu | |||
out_row[H_ALGO_SAMPLE_REGRET_CUMULATIVE] = cumulative_sample_regret | |||
out_row[H_ALGO_REGRET_EXPECTED] = expected_regret | |||
out_row[H_ALGO_REGRET_EXPECTED_CUMULATIVE] = cumulative_expected_regret | |||
pass |
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Function write_performance
refactored with the following changes:
- Remove redundant pass statement (
remove-redundant-pass
)
@@ -66,7 +66,6 @@ def write_performance(out_row, action, optimal_action, reward, sample_regret, cu | |||
out_row[H_ALGO_SAMPLE_REGRET_CUMULATIVE] = cumulative_sample_regret | |||
out_row[H_ALGO_REGRET_EXPECTED] = expected_regret | |||
out_row[H_ALGO_REGRET_EXPECTED_CUMULATIVE] = cumulative_expected_regret | |||
pass |
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Function write_performance
refactored with the following changes:
- Remove redundant pass statement (
remove-redundant-pass
)
# number of trials used to run Thompson Sampling to compute expectation stats | ||
# set to small value when debugging for faster speed | ||
num_trials_prob_best_action = int(1e4) | ||
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||
if models == None: | ||
if models is None: |
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Function calculate_thompson_single_bandit
refactored with the following changes:
- Move assignments closer to their usage (
move-assign
) - Use x is None rather than x == None (
none-compare
) - Replace index in for loop with direct reference (
for-index-replacement
) - Replace manual loop counter with call to enumerate (
convert-to-enumerate
) - Convert for loop into dictionary comprehension (
dict-comprehension
)
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