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update samples from Release-53 as a part of 1.19.0 SDK stable release

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amlrelsa-ms
2020-12-07 18:55:07 +00:00
parent 41366a4af0
commit 48e3e7b510
39 changed files with 371 additions and 279 deletions
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13
how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py
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@@ -3,11 +3,11 @@ from azureml.core import Environment
from azureml.core.conda_dependencies import CondaDependencies
from azureml.train.estimator import Estimator
from azureml.core.run import Run
from azureml.automl.core.shared import constants
def split_fraction_by_grain(df, fraction, time_column_name,
grain_column_names=None):
if not grain_column_names:
df['tmp_grain_column'] = 'grain'
grain_column_names = ['tmp_grain_column']
@@ -17,10 +17,10 @@ def split_fraction_by_grain(df, fraction, time_column_name,
.groupby(grain_column_names, group_keys=False))
df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-int(len(dfg) *
fraction)] if fraction > 0 else dfg)
fraction)] if fraction > 0 else dfg)
df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-int(len(dfg) *
fraction):] if fraction > 0 else dfg[:0])
fraction):] if fraction > 0 else dfg[:0])
if 'tmp_grain_column' in grain_column_names:
for df2 in (df, df_head, df_tail):
@@ -59,11 +59,13 @@ def get_result_df(remote_run):
'primary_metric', 'Score'])
goal_minimize = False
for run in children:
if('run_algorithm' in run.properties and 'score' in run.properties):
if run.get_status().lower() == constants.RunState.COMPLETE_RUN \
and 'run_algorithm' in run.properties and 'score' in run.properties:
# We only count in the completed child runs.
summary_df[run.id] = [run.id, run.properties['run_algorithm'],
run.properties['primary_metric'],
float(run.properties['score'])]
if('goal' in run.properties):
if ('goal' in run.properties):
goal_minimize = run.properties['goal'].split('_')[-1] == 'min'
summary_df = summary_df.T.sort_values(
@@ -118,7 +120,6 @@ def run_multiple_inferences(summary_df, train_experiment, test_experiment,
compute_target, script_folder, test_dataset,
lookback_dataset, max_horizon, target_column_name,
time_column_name, freq):
for run_name, run_summary in summary_df.iterrows():
print(run_name)
print(run_summary)