diff --git a/monitoring/dev/.buildinfo b/monitoring/dev/.buildinfo index 5be7e19db9..8647c816e2 100644 --- a/monitoring/dev/.buildinfo +++ b/monitoring/dev/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 1b021290ce2e88c85cb00797b2c4cae0 +config: 02fcecb19b168bb7aa4c1f2cdce3b3cc tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/monitoring/dev/_downloads/058b2cb05d55cde903f70a8d0a1721f3/auto_quickstarts_python.zip b/monitoring/dev/_downloads/058b2cb05d55cde903f70a8d0a1721f3/auto_quickstarts_python.zip index 04e3f14e96..445040fe68 100644 Binary files a/monitoring/dev/_downloads/058b2cb05d55cde903f70a8d0a1721f3/auto_quickstarts_python.zip and b/monitoring/dev/_downloads/058b2cb05d55cde903f70a8d0a1721f3/auto_quickstarts_python.zip differ diff --git a/monitoring/dev/_downloads/666cfa792f2210fcd4514277dbbaad12/auto_quickstarts_jupyter.zip b/monitoring/dev/_downloads/666cfa792f2210fcd4514277dbbaad12/auto_quickstarts_jupyter.zip index 5df4fa9e02..dc8ec8f6b5 100644 Binary files a/monitoring/dev/_downloads/666cfa792f2210fcd4514277dbbaad12/auto_quickstarts_jupyter.zip and b/monitoring/dev/_downloads/666cfa792f2210fcd4514277dbbaad12/auto_quickstarts_jupyter.zip differ diff --git a/monitoring/dev/_downloads/a6f12ce7ae7265694204d07a38ad8be8/auto_demos_jupyter.zip b/monitoring/dev/_downloads/a6f12ce7ae7265694204d07a38ad8be8/auto_demos_jupyter.zip index c57d7db992..cd4dfa7493 100644 Binary files a/monitoring/dev/_downloads/a6f12ce7ae7265694204d07a38ad8be8/auto_demos_jupyter.zip and b/monitoring/dev/_downloads/a6f12ce7ae7265694204d07a38ad8be8/auto_demos_jupyter.zip differ diff --git a/monitoring/dev/_downloads/cb472b7c29f435c5d316276afc17012a/auto_demos_python.zip b/monitoring/dev/_downloads/cb472b7c29f435c5d316276afc17012a/auto_demos_python.zip index 01a416f22a..e35012ae4b 100644 Binary files a/monitoring/dev/_downloads/cb472b7c29f435c5d316276afc17012a/auto_demos_python.zip and b/monitoring/dev/_downloads/cb472b7c29f435c5d316276afc17012a/auto_demos_python.zip differ diff --git a/monitoring/dev/_sources/user-guide/auto_demos/plot_lending_defaults.rst.txt b/monitoring/dev/_sources/user-guide/auto_demos/plot_lending_defaults.rst.txt index 7bf374c5e3..1029ac5152 100644 --- a/monitoring/dev/_sources/user-guide/auto_demos/plot_lending_defaults.rst.txt +++ b/monitoring/dev/_sources/user-guide/auto_demos/plot_lending_defaults.rst.txt @@ -477,21 +477,21 @@ We'll change the original timestamps so the samples are recent - ['2021-12-24 11:00:27.654846', '2022-01-24 11:00:27.654846', - '2022-02-24 11:00:27.654846', '2022-03-26 11:00:27.654846', - '2022-04-26 11:00:27.654846', '2022-05-26 11:00:27.654846', - '2022-06-26 11:00:27.654846', '2022-07-27 11:00:27.654846', - '2022-08-24 11:00:27.654846', '2022-09-24 11:00:27.654846', - '2022-10-24 11:00:27.654846', '2022-11-24 11:00:27.654846', - '2022-12-24 11:00:27.654846', '2023-01-24 11:00:27.654846', - '2023-02-24 11:00:27.654846', '2023-03-26 11:00:27.654846', - '2023-04-26 11:00:27.654846', '2023-05-26 11:00:27.654846', - '2023-06-26 11:00:27.654846', '2023-07-27 11:00:27.654846', - '2023-08-24 11:00:27.654846', '2023-09-24 11:00:27.654846', - '2023-10-24 11:00:27.654846', '2023-11-24 11:00:27.654846', - '2023-12-24 11:00:27.654846', '2024-01-24 11:00:27.654846', - '2024-02-24 11:00:27.654846', '2024-03-25 11:00:27.654846', - '2024-04-25 11:00:27.654846', '2024-05-25 11:00:27.654846'] + ['2021-12-25 08:36:18.020356', '2022-01-25 08:36:18.020356', + '2022-02-25 08:36:18.020356', '2022-03-27 08:36:18.020356', + '2022-04-27 08:36:18.020356', '2022-05-27 08:36:18.020356', + '2022-06-27 08:36:18.020356', '2022-07-28 08:36:18.020356', + '2022-08-25 08:36:18.020356', '2022-09-25 08:36:18.020356', + '2022-10-25 08:36:18.020356', '2022-11-25 08:36:18.020356', + '2022-12-25 08:36:18.020356', '2023-01-25 08:36:18.020356', + '2023-02-25 08:36:18.020356', '2023-03-27 08:36:18.020356', + '2023-04-27 08:36:18.020356', '2023-05-27 08:36:18.020356', + '2023-06-27 08:36:18.020356', '2023-07-28 08:36:18.020356', + '2023-08-25 08:36:18.020356', '2023-09-25 08:36:18.020356', + '2023-10-25 08:36:18.020356', '2023-11-25 08:36:18.020356', + '2023-12-25 08:36:18.020356', '2024-01-25 08:36:18.020356', + '2024-02-25 08:36:18.020356', '2024-03-26 08:36:18.020356', + '2024-04-26 08:36:18.020356', '2024-05-26 08:36:18.020356'] Length: 30, dtype: datetime64[ns] @@ -719,7 +719,7 @@ CAUTION: This will delete the model, all model versions, and all associated data .. rst-class:: sphx-glr-timing - **Total running time of the script:** (3 minutes 45.406 seconds) + **Total running time of the script:** (3 minutes 50.324 seconds) .. _sphx_glr_download_user-guide_auto_demos_plot_lending_defaults.py: diff --git a/monitoring/dev/_sources/user-guide/auto_demos/plot_rent_prediction.rst.txt b/monitoring/dev/_sources/user-guide/auto_demos/plot_rent_prediction.rst.txt index b6ef968b79..be8b427265 100644 --- a/monitoring/dev/_sources/user-guide/auto_demos/plot_rent_prediction.rst.txt +++ b/monitoring/dev/_sources/user-guide/auto_demos/plot_rent_prediction.rst.txt @@ -414,7 +414,7 @@ Let's start by uploading the first part of the dataset 10000 new samples sent. 10000 new samples sent. 10000 new samples sent. - 5007 new samples sent. + 5018 new samples sent. Upload finished successfully but might take time to ingest into the system, see http://127.0.0.1:8000/configuration/models for status. @@ -450,7 +450,7 @@ Now let's upload the second half of the dataset. 10000 new samples sent. 10000 new samples sent. - 4993 new samples sent. + 4982 new samples sent. Upload finished successfully but might take time to ingest into the system, see http://127.0.0.1:8000/configuration/models for status. @@ -597,7 +597,7 @@ CAUTION: This will delete the model, all model versions, and all associated data .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 49.860 seconds) + **Total running time of the script:** (0 minutes 46.664 seconds) .. _sphx_glr_download_user-guide_auto_demos_plot_rent_prediction.py: diff --git a/monitoring/dev/_sources/user-guide/auto_demos/sg_execution_times.rst.txt b/monitoring/dev/_sources/user-guide/auto_demos/sg_execution_times.rst.txt index d7f78a7306..d09fee18a4 100644 --- a/monitoring/dev/_sources/user-guide/auto_demos/sg_execution_times.rst.txt +++ b/monitoring/dev/_sources/user-guide/auto_demos/sg_execution_times.rst.txt @@ -6,10 +6,10 @@ Computation times ================= -**04:35.266** total execution time for **user-guide_auto_demos** files: +**04:36.987** total execution time for **user-guide_auto_demos** files: +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_user-guide_auto_demos_plot_lending_defaults.py` (``plot_lending_defaults.py``) | 03:45.406 | 0.0 MB | +| :ref:`sphx_glr_user-guide_auto_demos_plot_lending_defaults.py` (``plot_lending_defaults.py``) | 03:50.324 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_user-guide_auto_demos_plot_rent_prediction.py` (``plot_rent_prediction.py``) | 00:49.860 | 0.0 MB | +| :ref:`sphx_glr_user-guide_auto_demos_plot_rent_prediction.py` (``plot_rent_prediction.py``) | 00:46.664 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/plot_prepare_your_data.rst.txt b/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/plot_prepare_your_data.rst.txt index f863be5e56..eca66fe376 100644 --- a/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/plot_prepare_your_data.rst.txt +++ b/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/plot_prepare_your_data.rst.txt @@ -411,7 +411,7 @@ it is preferable to provide the predicted probabilities per class rather than th .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 3.076 seconds) + **Total running time of the script:** (0 minutes 8.581 seconds) .. _sphx_glr_download_user-guide_tabular_auto_quickstarts_plot_prepare_your_data.py: diff --git a/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/plot_quickstart.rst.txt b/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/plot_quickstart.rst.txt index 405a0b9523..ee27750966 100644 --- a/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/plot_quickstart.rst.txt +++ b/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/plot_quickstart.rst.txt @@ -334,7 +334,7 @@ If we wish to remove the model do free up space for new models we can do it in t .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 51.102 seconds) + **Total running time of the script:** (0 minutes 48.815 seconds) .. _sphx_glr_download_user-guide_tabular_auto_quickstarts_plot_quickstart.py: diff --git a/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/sg_execution_times.rst.txt b/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/sg_execution_times.rst.txt index 8a6edc5123..988effbd81 100644 --- a/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/sg_execution_times.rst.txt +++ b/monitoring/dev/_sources/user-guide/tabular/auto_quickstarts/sg_execution_times.rst.txt @@ -6,10 +6,10 @@ Computation times ================= -**01:54.178** total execution time for **user-guide_tabular_auto_quickstarts** files: +**00:57.396** total execution time for **user-guide_tabular_auto_quickstarts** files: +---------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_user-guide_tabular_auto_quickstarts_plot_prepare_your_data.py` (``plot_prepare_your_data.py``) | 01:03.076 | 0.0 MB | +| :ref:`sphx_glr_user-guide_tabular_auto_quickstarts_plot_quickstart.py` (``plot_quickstart.py``) | 00:48.815 | 0.0 MB | +---------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_user-guide_tabular_auto_quickstarts_plot_quickstart.py` (``plot_quickstart.py``) | 00:51.102 | 0.0 MB | +| :ref:`sphx_glr_user-guide_tabular_auto_quickstarts_plot_prepare_your_data.py` (``plot_prepare_your_data.py``) | 00:08.581 | 0.0 MB | +---------------------------------------------------------------------------------------------------------------+-----------+--------+ diff 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\ No newline at end of file diff --git a/monitoring/dev/user-guide/auto_demos/plot_lending_defaults.html b/monitoring/dev/user-guide/auto_demos/plot_lending_defaults.html index f9382f3db9..01f1742f1c 100644 --- a/monitoring/dev/user-guide/auto_demos/plot_lending_defaults.html +++ b/monitoring/dev/user-guide/auto_demos/plot_lending_defaults.html @@ -539,8 +539,8 @@

Getting the Data
import pandas as pd
-train_df = pd.read_csv('https://figshare.com/ndownloader/files/39316160')
-train_df.head(2)
+train_df = pd.read_csv('https://figshare.com/ndownloader/files/39316160')
+train_df.head(2)
@@ -691,7 +691,7 @@

Data Schema'label': 'loan_status', 'datetime_name': 'issue_d' } -train_dataset = Dataset(train_df, **dataset_kwargs) +train_dataset = Dataset(train_df, **dataset_kwargs)

We’ll create the schema file, and print it to show (and validate) the schema that was created.

@@ -726,7 +726,7 @@

Feature Importancewith urlopen('https://figshare.com/ndownloader/files/39316172') as f: model = joblib.load(f) -feature_importance = pd.Series(model.feature_importances_ / sum(model.feature_importances_), index=model.feature_names_) +feature_importance = pd.Series(model.feature_importances_ / sum(model.feature_importances_), index=model.feature_names_) @@ -739,8 +739,8 @@

Creating a Model Version
ref_predictions = model.predict(train_df[features])
-ref_predictions_proba = model.predict_proba(train_df[features])
+
ref_predictions = model.predict(train_df[features])
+ref_predictions_proba = model.predict_proba(train_df[features])
 
 model_name = 'Loan Defaults - Example'
 

@@ -750,7 +750,7 @@ 
Creating a Model Version
model_version = dc_client.create_tabular_model_version(model_name=model_name, version_name='ver_1',
                                                        schema=schema_file_path,
-                                                       feature_importance=feature_importance,
+                                                       feature_importance=feature_importance,
                                                        reference_dataset=train_dataset,
                                                        reference_predictions=ref_predictions,
                                                        reference_probas=ref_predictions_proba,
@@ -776,46 +776,46 @@ 
Uploading Production DataProduction Data Guide. Here we’ll show how to use the batch upload
 method. In our example we already saved the production data for all time stamps in a csv file on figshare.

-
prod_data = pd.read_csv('https://figshare.com/ndownloader/files/39316157', parse_dates=['issue_d'])
+
prod_data = pd.read_csv('https://figshare.com/ndownloader/files/39316157', parse_dates=['issue_d'])
 

 

 
We’ll change the original timestamps so the samples are recent

 
import datetime
-time_delta = pd.Timedelta(pd.to_datetime(datetime.datetime.now()) - prod_data['issue_d'].max()) - pd.Timedelta(2, unit='d')
-prod_data['issue_d'] = prod_data['issue_d'] + time_delta
-prod_data['issue_d'].unique()
+time_delta = pd.Timedelta(pd.to_datetime(datetime.datetime.now()) - prod_data['issue_d'].max()) - pd.Timedelta(2, unit='d')
+prod_data['issue_d'] = prod_data['issue_d'] + time_delta
+prod_data['issue_d'].unique()
 

 

 
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- '2023-04-26 11:00:27.654846', '2023-05-26 11:00:27.654846',
- '2023-06-26 11:00:27.654846', '2023-07-27 11:00:27.654846',
- '2023-08-24 11:00:27.654846', '2023-09-24 11:00:27.654846',
- '2023-10-24 11:00:27.654846', '2023-11-24 11:00:27.654846',
- '2023-12-24 11:00:27.654846', '2024-01-24 11:00:27.654846',
- '2024-02-24 11:00:27.654846', '2024-03-25 11:00:27.654846',
- '2024-04-25 11:00:27.654846', '2024-05-25 11:00:27.654846']
+['2021-12-25 08:36:18.020356', '2022-01-25 08:36:18.020356',
+ '2022-02-25 08:36:18.020356', '2022-03-27 08:36:18.020356',
+ '2022-04-27 08:36:18.020356', '2022-05-27 08:36:18.020356',
+ '2022-06-27 08:36:18.020356', '2022-07-28 08:36:18.020356',
+ '2022-08-25 08:36:18.020356', '2022-09-25 08:36:18.020356',
+ '2022-10-25 08:36:18.020356', '2022-11-25 08:36:18.020356',
+ '2022-12-25 08:36:18.020356', '2023-01-25 08:36:18.020356',
+ '2023-02-25 08:36:18.020356', '2023-03-27 08:36:18.020356',
+ '2023-04-27 08:36:18.020356', '2023-05-27 08:36:18.020356',
+ '2023-06-27 08:36:18.020356', '2023-07-28 08:36:18.020356',
+ '2023-08-25 08:36:18.020356', '2023-09-25 08:36:18.020356',
+ '2023-10-25 08:36:18.020356', '2023-11-25 08:36:18.020356',
+ '2023-12-25 08:36:18.020356', '2024-01-25 08:36:18.020356',
+ '2024-02-25 08:36:18.020356', '2024-03-26 08:36:18.020356',
+ '2024-04-26 08:36:18.020356', '2024-05-26 08:36:18.020356']
 Length: 30, dtype: datetime64[ns]
 

 

 

 
Uploading a Batch of Data#

-
prod_predictions = model.predict(prod_data[train_dataset.features].fillna('NONE'))
-prod_prediction_probas = model.predict_proba(prod_data[train_dataset.features].fillna('NONE'))
+
prod_predictions = model.predict(prod_data[train_dataset.features].fillna('NONE'))
+prod_prediction_probas = model.predict_proba(prod_data[train_dataset.features].fillna('NONE'))
 

 

 
We’ll also convert out pandas datetime column to int timestamps (seconds since epoch), which is the time format
 expected by deepchecks.

-
model_version.log_batch(sample_ids=prod_data['id'],
-                        data=prod_data.drop(['issue_d', 'id', 'loan_status'], axis=1),
-                        timestamps=(prod_data['issue_d'].astype(int) // 1e9).astype(int),
+
model_version.log_batch(sample_ids=prod_data['id'],
+                        data=prod_data.drop(['issue_d', 'id', 'loan_status'], axis=1),
+                        timestamps=(prod_data['issue_d'].astype(int) // 1e9).astype(int),
                         predictions=prod_predictions,
                         prediction_probas=prod_prediction_probas)
 

@@ -839,7 +839,7 @@ 
Uploading The LabelsThe labels are global to the model, and not specific to a version. Therefore, to upload them we need the model client.
 You can do this directly after uploading the predictions, or at any other time.

 
model_client = dc_client.get_or_create_model(model_name)
-model_client.log_batch_labels(sample_ids=prod_data['id'], labels=prod_data['loan_status'])
+model_client.log_batch_labels(sample_ids=prod_data['id'], labels=prod_data['loan_status'])
 

 

 
10000 labels sent.
@@ -930,7 +930,7 @@ 
Cleaning up
# dc_client.delete_model(model_name)
 

 

-
Total running time of the script: (3 minutes 45.406 seconds)

+
Total running time of the script: (3 minutes 50.324 seconds)

 

@@ -696,7 +696,7 @@ 
Creating a Model Versionmodel_version = dc_client.create_tabular_model_version(model_name=model_name, version_name='ver_1',
                                                        schema=schema_file_path,
-                                                       feature_importance=feature_importance,
+                                                       feature_importance=feature_importance,
                                                        reference_dataset=train_dataset,
                                                        reference_predictions=ref_predictions,
                                                        task_type='regression')
@@ -715,7 +715,7 @@ 
Uploading Production DataProduction Data Guide. Here we’ll
 show how to use the batch upload method.

-
prod_data, prod_predictions = load_data_and_predictions(data_format='DataFrame', load_train=False, data_size=100_000)
+
prod_data, prod_predictions = load_data_and_predictions(data_format='DataFrame', load_train=False, data_size=100_000)
 

 

 

@@ -723,16 +723,16 @@ 
Uploading a Batch of Data
 
Uploading the First Batch#

 
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()
+
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.timestamp < end_of_first_half]
-second_half_df = prod_data[prod_data.timestamp >= 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),
-                        timestamps=first_half_df[timestamp], predictions=prod_predictions[:len(first_half_df)])
+model_version.log_batch(sample_ids=first_half_df.index,
+                        data=first_half_df.drop([timestamp, label_col], axis=1),
+                        timestamps=first_half_df[timestamp], predictions=prod_predictions[:len(first_half_df)])
 

 

 
/home/runner/work/monitoring/monitoring/.venv/lib/python3.11/site-packages/deepchecks_client/tabular/client.py:681: UserWarning:
@@ -746,7 +746,7 @@ 
Uploading the First Batch
 
Uploading the Second Batch#

 
Now let’s upload the second half of the dataset.

-
model_version.log_batch(sample_ids=second_half_df.index,
-                        data=second_half_df.drop([timestamp, label_col], axis=1),
-                        timestamps=second_half_df[timestamp], predictions=prod_predictions[len(first_half_df):])
+
model_version.log_batch(sample_ids=second_half_df.index,
+                        data=second_half_df.drop([timestamp, label_col], axis=1),
+                        timestamps=second_half_df[timestamp], predictions=prod_predictions[len(first_half_df):])
 

 

 
/home/runner/work/monitoring/monitoring/.venv/lib/python3.11/site-packages/deepchecks_client/tabular/client.py:681: UserWarning:
@@ -765,7 +765,7 @@ 
Uploading the Second BatchThe labels are global to the model, and not specific to a version. Therefore, to upload them we need the model client.
 You can do this directly after uploading the predictions, or at any other time.

 
model_client = dc_client.get_or_create_model(model_name)
-model_client.log_batch_labels(sample_ids=prod_data.index, labels=prod_data[label_col])
+model_client.log_batch_labels(sample_ids=prod_data.index, labels=prod_data[label_col])
 

 

 
10000 labels sent.
@@ -862,7 +862,7 @@ 
Cleaning up
# dc_client.delete_model(model_name)
 

 

-
Total running time of the script: (0 minutes 49.860 seconds)

+
Total running time of the script: (0 minutes 46.664 seconds)