Jason Tam JasonTam

## neg_samp.py
"""
Pretty efficient way to sample negatives using binary search.
Imagine `pos_inds` are items a user has interacted with,
and we want a vectorized approach to uniformly sample many negative items.
This is better than (in the case where `n_samp` is large)
sampling from the entire catalog and then checking if it's a positive item.

Ahem... negative sampling for sparse interaction matrices.
"""

## neg_samp_experiments.ipynb

      
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              4 stars
            
          
                JasonTam
                / neg_samp_experiments.ipynb
            
            
              Last active
              May 19, 2023 15:03
            
          
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## tf_inspect_checkpoint_example.py
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file

print_tensors_in_checkpoint_file(
    file_name='/tmp/tensorboard-logs/tophat-movielens/20180530T140423/model.ckpt',
    tensor_name=None, all_tensors=False,
    all_tensor_names=True,
    )

## printer_shit
Brother HL-2140 Foomatic/hpijs-pcl5e

sudo apt-get install hpijs-ppds printer-driver-hpijs

## spark_date_filter.scala
val nDays = 450

df
  .withColumn("date_merge", concat_ws("-", $"year", $"month", $"day"))
  .withColumn("date", to_date(unix_timestamp($"date_merge", "yyyy-MM-dd").cast("timestamp")))
  .drop("date_merge")
  .where($"date" >= date_add(current_date(), -nDays)))

## tf_serving.conf
model_config_list: {
  config: {
    name: "bag",
    base_path: "s3://.../bag_model",
    model_platform: "tensorflow",
    model_version_policy: {
      all: {}
    }
  },
  config: {

## winners_curse.ipynb

      
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                JasonTam
                / winners_curse.ipynb
            
            
              Created
              October 23, 2018 06:08
            
              
                https://medium.com/airbnb-engineering/selection-bias-in-online-experimentation-c3d67795cceb
              
          
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## shap_order.ipynb

      
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                JasonTam
                / shap_order.ipynb
            
            
              Created
              November 5, 2018 02:08
            
              
                custom strftime xticks and ordering keys for shap forceplot
              
          
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## dummy_a.json
["a1", "a2", "a3", "a4", "a5", "a6"]

## dummy_b.json
["b1", "b2", "b3", "b4", "b5", "b6"]
	"""
	Pretty efficient way to sample negatives using binary search.
	Imagine `pos_inds` are items a user has interacted with,
	and we want a vectorized approach to uniformly sample many negative items.
	This is better than (in the case where `n_samp` is large)
	sampling from the entire catalog and then checking if it's a positive item.

	Ahem... negative sampling for sparse interaction matrices.
	"""
	from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file

	print_tensors_in_checkpoint_file(
	file_name='/tmp/tensorboard-logs/tophat-movielens/20180530T140423/model.ckpt',
	tensor_name=None, all_tensors=False,
	all_tensor_names=True,
	)
	Brother HL-2140 Foomatic/hpijs-pcl5e

	sudo apt-get install hpijs-ppds printer-driver-hpijs
	val nDays = 450

	df
	.withColumn("date_merge", concat_ws("-", $"year", $"month", $"day"))
	.withColumn("date", to_date(unix_timestamp($"date_merge", "yyyy-MM-dd").cast("timestamp")))
	.drop("date_merge")
	.where($"date" >= date_add(current_date(), -nDays)))
	model_config_list: {
	config: {
	name: "bag",
	base_path: "s3://.../bag_model",
	model_platform: "tensorflow",
	model_version_policy: {
	all: {}
	}
	},
	config: {