Skip to content

Fixing CI #10

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
ItayGabbay merged 4 commits into from
Apr 9, 2023
Merged

Fixing CI #10

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
cc8c60f
Fixing CI
Apr 9, 2023
4191b72
Fixing docs
Apr 9, 2023
eb5edb3
Fixing docs
Apr 9, 2023
9232800
Fixing docs
Apr 9, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 3 additions & 0 deletions .development.env
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,7 @@
LAUCHDARKLY_SDK_KEY=
oauth_url=https://deepchecks-monitoring.eu.auth0.com
OAUTH_CLIENT_ID=
OAUTH_CLIENT_SECRET=
email_smtp_host=email-smtp.eu-west-1.amazonaws.com
email_smtp_port=25
email_smtp_username=
Expand Down
2 changes: 1 addition & 1 deletion backend/VERSION
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1 +1 @@
0.12.0
0.15.1
31 changes: 13 additions & 18 deletions docs/source/user-guide/demos/plot_rent_prediction.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -77,15 +77,16 @@
# We'll start by downloading the training data from the deepchecks testing package. This training data will be used
# to set the reference for the model version. We'll also download the pre-calculated predictions for this data.

from deepchecks.tabular.datasets.regression.airbnb import load_data_large
from deepchecks.tabular.datasets.regression.airbnb import load_data, load_pre_calculated_prediction

train_df, ref_predictions = load_data_large(data_format='DataFrame')
train_df.head(2)
ref_df, _ = load_data(data_format='Dataframe')
ref_predictions, _ = load_pre_calculated_prediction()
ref_df.head(2)

# %%
# So what do we have? Let's note the special columns in our data:
#
# 1. datestamp - The timestamp of the sample (seconds since epoch)
# 1. timestamp - The timestamp of the sample (seconds since epoch)
# 2. price - Our label
#
# All the other columns are features that can be used by our model to predict the price. We note that there are some
Expand All @@ -106,13 +107,12 @@
# together with metadata about the role of each column.

from deepchecks.tabular import Dataset
timestamp, label_col = 'datestamp', 'price'
timestamp, label_col = 'timestamp', 'price'
train_dataset = Dataset(
train_df, label=label_col,
ref_df, label=label_col,
features=['room_type', 'neighbourhood', 'neighbourhood_group', 'has_availability', 'minimum_nights',
'number_of_reviews', 'reviews_per_month', 'calculated_host_listings_count', 'availability_365'],
cat_features=['neighbourhood_group', 'neighbourhood', 'room_type', 'has_availability'],
datetime_name=timestamp)
cat_features=['neighbourhood_group', 'neighbourhood', 'room_type', 'has_availability'])

# %%
# We'll create the schema file, and print it to show (and validate) the schema that was created.
Expand Down Expand Up @@ -174,14 +174,8 @@
# read more, refer to the :doc:`Production Data Guide </user-guide/tabular/tabular-production>`. Here we'll
# show how to use the batch upload method.

prod_data, prod_predictions = load_data_large(data_format='DataFrame', load_train=False)

# %%
# We'll change the original timestamps so the samples are recent

import datetime
yesterdays_timestamp = int(datetime.datetime.now().timestamp()) - 3600*24
prod_data[timestamp] = prod_data[timestamp] + (yesterdays_timestamp - prod_data[timestamp].max())
_, prod_data = load_data(data_format='DataFrame')
_, prod_predictions = load_pre_calculated_prediction()

# %%
# Uploading a Batch of Data
Expand All @@ -193,11 +187,12 @@
#
# Let's start by uploading the first part of the dataset

prod_data[timestamp] = prod_data[timestamp].astype(int) // 10 ** 9 # Convert to second-based epoch time
timestamps = prod_data[timestamp].unique()
end_of_first_half = timestamps[3 * int(len(timestamps) // 4)] # This is the first 3 weeks of the production data

first_half_df = prod_data[prod_data.datestamp < end_of_first_half]
second_half_df = prod_data[prod_data.datestamp >= end_of_first_half]
first_half_df = prod_data[prod_data.timestamp < end_of_first_half]
second_half_df = prod_data[prod_data.timestamp >= end_of_first_half]

model_version.log_batch(sample_ids=first_half_df.index,
data=first_half_df.drop([timestamp, label_col], axis=1),
Expand Down
10 changes: 5 additions & 5 deletions makefile
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -315,12 +315,12 @@ external-services-setup:
@sleep 2

env-setup: external-services-setup
@docker run -d --env-file .development.env --network deepchecks -p 8000:8000 deepchecks-enterprise-testing start-test.sh
@docker run -d --env-file .development.env -e LAUCHDARKLY_SDK_KEY -e OAUTH_CLIENT_ID -e OAUTH_CLIENT_SECRET --network deepchecks -p 8000:8000 deepchecks-enterprise-testing start-test.sh
@sleep 15
@docker run -d --env-file .development.env --network deepchecks deepchecks-enterprise-testing start-alert-scheduler.sh
@docker run -d --env-file .development.env --network deepchecks deepchecks-enterprise-testing start-worker.sh
@docker run -d --env-file .development.env --network deepchecks deepchecks-enterprise-testing start-task-queuer.sh
@docker run -d --env-file .development.env --network deepchecks deepchecks-enterprise-testing start-task-runner.sh
@docker run -d --env-file .development.env -e LAUCHDARKLY_SDK_KEY --network deepchecks deepchecks-enterprise-testing start-alert-scheduler.sh
@docker run -d --env-file .development.env -e LAUCHDARKLY_SDK_KEY --network deepchecks deepchecks-enterprise-testing start-worker.sh
@docker run -d --env-file .development.env -e LAUCHDARKLY_SDK_KEY --network deepchecks deepchecks-enterprise-testing start-task-queuer.sh
@docker run -d --env-file .development.env -e LAUCHDARKLY_SDK_KEY --network deepchecks deepchecks-enterprise-testing start-task-runner.sh
@sleep 10

cypress: env-setup
Expand Down