Datasets:
celinelee
/
python_fn_calls

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fillna(method='ffill')
fillna(method='bfill')
stage_model.predict(df2.drop(["ds","model"], axis=1)
pd.concat([df1,df2])
reset_index()
px.line(df, x="ds", y="yhat", color='model', title=f"{airport_code} delay forecast by model stage", labels=labels)
fig.show()
EXTRACT(year from a.hour)
EXTRACT(dayofweek from a.hour)
EXTRACT(hour from a.hour)
COALESCE(c.tot_precip_mm,0)
TO_DATE(a.hour)
format((datetime.strptime(end_date, '%Y-%m-%d')
timedelta(hours=int(hours_to_forecast)
strftime("%Y-%m-%d %H:%M:%S")
dbutils.widgets.get('00.Airport_Code')
format(airport_id)
client.search_model_versions(f"name='{model_name}'")
dict(mv)
dict(mv)
dict(mv)
dict(mv)
mlflow.sklearn.load_model(f"models:/{model_name}/Production")
spark.sql(f"""SELECT * from citibike.forecast_regression_timeweather WHERE station_id = '{station_id}' and model_version = '{prod_version}';""")
toPandas()
mlflow.sklearn.load_model(f"models:/{model_name}/Staging")
spark.sql(f"""SELECT * from citibike.forecast_regression_timeweather WHERE station_id = '{station_id}' and model_version = '{stage_version}';""")
toPandas()
pd.concat([pdf,sdf])
fig.show()
warnings.filterwarnings("ignore", message=r"Passing", category=FutureWarning)
tfdv.generate_statistics_from_dataframe(dataframe=train_df.toPandas()
tfdv.generate_statistics_from_dataframe(dataframe=fdf.toPandas()
tfdv.infer_schema(statistics=stats_train)
tfdv.display_schema(schema=schema)
tfdv.validate_statistics(statistics=stats_serve, schema=schema)
tfdv.display_anomalies(anomalies)
tfdv.get_feature(schema, 'temp_f')
tfdv.get_feature(schema, 'precip_mm')
tfdv.validate_statistics(stats_train, schema, serving_statistics=stats_serve)
tfdv.get_feature(schema, 'hour')
tfdv.get_feature(schema, 'dayofweek')
tfdv.validate_statistics(stats_train, schema, serving_statistics=stats_serve)
tfdv.display_anomalies(_anomalies)
dbutils.notebook.exit(json.dumps({"exit_code": "Success"})
Canon. (2016)
L. (2012)
Copyright (C)
log_encoding_CanonLog(0.18)
np.array([0, 2, 18, 90, 720])
np.around(log_encoding_CanonLog(x)
astype(np.int)
array([ 128, 169, 351, 614, 1016])
np.around(log_encoding_CanonLog(x, 10, False)
array([ 7.3, 12. , 32.8, 62.7, 108.7])
to_domain_1(x)
domain_range_scale("ignore")
log_decoding_CanonLog(0.0730597, bit_depth, False)
np.log10(-x * 10.1596 + 1)
np.log10(10.1596 * x + 1)
full_to_legal(clog, bit_depth)
as_float(from_range_1(clog_cv)
log_decoding_CanonLog(34.338965172606912 / 100)
to_domain_1(clog)
legal_to_full(clog, bit_depth)
as_float(from_range_1(x)
log_encoding_CanonLog2(0.18)
to_domain_1(x)
domain_range_scale("ignore")
log_decoding_CanonLog2(0.035388128, bit_depth, False)
np.log10(-x * 87.09937546 + 1)
np.log10(x * 87.09937546 + 1)
full_to_legal(clog2, bit_depth)
as_float(from_range_1(clog2_cv)
log_decoding_CanonLog2(39.825469498316735 / 100)
to_domain_1(clog2)
legal_to_full(clog2, bit_depth)
as_float(from_range_1(x)
N. (2018)
log_encoding_CanonLog3(0.18)
to_domain_1(x)
domain_range_scale("ignore")
log_decoding_CanonLog3(0.04076162, bit_depth, False, False)
log_decoding_CanonLog3(0.105357102, bit_depth, False, False)
np.log10(-x * 14.98325 + 1)
np.log10(x * 14.98325 + 1)
full_to_legal(clog3, bit_depth)
as_float(from_range_1(clog3_cv)
log_decoding_CanonLog3(34.338936938868677 / 100)
to_domain_1(clog3)
legal_to_full(clog3, bit_depth)
as_float(from_range_1(x)
Copyright (c)
files (the "Software")
InsertListOnlineRequest(BaseRequestObject)
HTML (or TXT)
__init__(self, document, list_insert, load_encoding=None, password=None, dest_file_name=None, revision_author=None, revision_date_time=None)
create_http_request(self, api_client)
ValueError("Missing the required parameter `document` when calling `insert_list_online`")
ValueError("Missing the required parameter `list_insert` when calling `insert_list_online`")