seahrh

// Copyright 2007, Google Inc.
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
//     * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//     * Redistributions in binary form must reproduce the above

seahrh

model.call = tf.function(model.call, experimental_relax_shapes=True)
for (test_df, pred_df) in tqdm(env.iter_test()):
    pred = model(data, training = False).numpy()

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aws s3 ls --summarize --human-readable --recursive s3://mybucket/mydir/

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CREATE OR REPLACE PROCEDURE sp_update_ddl()
AS $$
	BEGIN
		alter table myschema.mytable add column newcolumn bigint;
	EXCEPTION
		WHEN OTHERS THEN
			raise 'exception in filename.sql';
	END
$$ LANGUAGE plpgsql
;

seahrh

definitions:
    steps:
      - step: &tests
          image: python:3.7.9
          script:
            - pip install ."[tests]"
            - mypy src
            - pytest -vv --cov=src
          caches:
            - pip

seahrh

import os
import numpy as np
from matplotlib import pyplot as plt
import IPython.display as ipd
import librosa
import pandas as pd
%matplotlib inline

def print_plot_play(x, Fs, text=''):
    """1. Prints information about an audio singal, 2. plots the waveform, and 3. Creates player

seahrh

import os
import numpy as np
from matplotlib import pyplot as plt
import IPython.display as ipd
import librosa
import pandas as pd
%matplotlib inline

def print_plot_play(x, Fs, text=''):
    """1. Prints information about an audio singal, 2. plots the waveform, and 3. Creates player

seahrh

def recall(y_true, y_pred):
    y_true = K.ones_like(y_true) 
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    all_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    
    recall = true_positives / (all_positives + K.epsilon())
    return recall

def precision(y_true, y_pred):
    y_true = K.ones_like(y_true) 

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feature_important = clf.get_booster().get_score(importance_type="weight")
keys = list(feature_important.keys())
values = list(feature_important.values())

data = pd.DataFrame(data=values, index=keys, columns=["score"]).sort_values(by = "score", ascending=False)

# Top 10 features
data.head(20)

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-- see https://docs.aws.amazon.com/redshift/latest/dg/r_PG_TABLE_DEF.html

select "column", type, encoding, distkey, sortkey
from pg_table_def where schemaname='public' and tablename='mytable' and sortkey!=0;