20200402 oracle cloud infrastructure data science

2020 4 2
Oracle Cloud Infrastructure Data Science

Oracle Java Oracle CorporationOracle Java Oracle Corporation
2 Copyright © 2020 Oracle and/or its affiliates.

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• (ML) OSS
Oracle Accelerated Data
Science(ADS)
• ML
• PaaS IaaS
Overview

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• Notebook
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• Jupyter Notebook ML
Compute
• Compartment VCN Subnet Compute
Block Volume
• ML
• Keras
• scikit-learn
• XGBoost
• Oracle Accelerated Data Science(ADS)
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•
Accelerated Data Science
scikit-learn
ML
Jupyter Notebook
Noteboot
Compute Block Storage

Notebook
Python
Notebook
OCI OCI Jupyter Notebook

• Oracle Cloud Infrastructure Data Science
Python
•
API
• Oracle AutoML
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Oracle Accelerated Data Science(ADS)
AutoML
Confidential – © 2020 Oracle Internal
⑥モデルの
解釈
②データの
変換
⑤モデルの
評価
Accelerated
data
Science

• ADS
• DatasetFactory
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• OCI Object Storage, Amazon S3, Google Cloud Storage, Azure Blob
• Oracle DB, ADW, MongoDB, HDFS, NoSQL DB, Elastic Search, etc.
•
• CSV, TSV, Parquet, libsvm, json, Excel, HDF5, SQL, xml, Apache Server Logfile(clf, log), arff

#
ds = DatasetFactory.open("/path/to/data.data", format='csv', delimiter=" ")
# OCI Object Storage Service
ds = DatasetFactory.open("oci://<bucket-name>/<file-name>", storage_options = {
"config": "~/.oci/config",
"profile": "DEFAULT_USER"
})
# Amazon S3
ds = DatasetFactory.open("s3://bucket_name/iris.csv", storage_options = {
'key': 'aws key',
'secret': 'aws secret,
'blocksize': 1000000,
'client_kwargs': {
"endpoint_url": "https://s3-us-west-1.amazonaws.com"
}
})
# ADW
uri = f'oracle+cx_oracle://{os.environ["ADW_USER"]}:{os.environ["ADW_PASSWORD"]}@{os.environ["ADW_SID"]}’
ds = DatasetFactory.open(uri, format="sql", table=table, index_col=index_col, target='label')

• RDB
• ( )
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• ” ”
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• etc.

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• String
• ( )
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• Null Null

1.
2.
3.
4.
ADS
#
ds.get_recommendations()
transformed_ds = ds.get_transformed_dataset()
#
transformed_ds = ds.auto_transform()
ADS AutoML

ADS
( , )
( , )
“Drop”
get_recommendations()

( , )
( , )
“Drop”

( )
( , )
“Up-sample” “Down-sample”
( , )

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• API(Seaborn, Matplotlib, GIS)

# show_in_notebook()
ds.show_in_notebook()
5

#
ds.plot("col02").show_in_notebook(figsize=(4,4))
#
ds.plot("col02", y="col01").show_in_notebook(figsize=(4,4))
#
ds.plot("col01", y="col03").show_in_notebook()

API
# Matplotlib
from numpy.random import randn
df = pd.DataFrame(randn(1000, 4), columns=list('ABCD'))
def ts_plot(df, figsize):
ts = pd.Series(randn(1000), index=pd.date_range('1/1/2000',
periods=1000))
df.set_index(ts)
df = df.cumsum()
plt.figure()
df.plot(figsize=figsize)
plt.legend(loc='best')
ds = DatasetFactory.from_dataframe(df, target='A')
ds.call(ts_plot, figsize=(7,7))
Seaborn, Matplotlib, GIS

• ADS AutoML
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1.
2. ( )
3.
4.
#
train, test = transformed_ds.train_test_split(test_size=0.1)
#
ml_engine = OracleAutoMLProvider(n_jobs=-1, loglevel=logging.ERROR)
oracle_automl = AutoML(train, provider=ml_engine)
automl_model1, baseline = oracle_automl.train()
• AdaBoostClassifier
• DecisionTreeClassifier
• ExtraTreesClassifier
• KNeighborsClassifier
• LGBMClassifier
• LinearSVC
• LogisticRegression
• RandomForestClassifier
• SVC
• XGBClassifier

Oracle AutoML
oracle_automl.visualize_algorithm_selection_trials() oracle_automl.visualize_adaptive_sampling_trials()

Oracle AutoML
oracle_automl.visualize_feature_selection_trials() oracle_automl.visualize_tuning_trials()

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•
• ( )
TESTTESTTESTTESTTEST TRAIN TESTTESTTESTTESTTEST TRAIN
TRAIN TEST
TRAIN TRAINTEST
TRAINTEST
TRAINTEST
(※1)
1
2
3
4
5
※1 N 1 1 TEST N-1
TRAIN 2 1 TEST N-1 TRAIN
N

)
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• PR ROC
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#
bin_evaluator = ADSEvaluator(test, models=[bin_lr_model, bin_rf_model],
training_data=train)
#
bin_evaluator.show_in_notebook(perfect=True)

25 Copyright © 2020 Oracle
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• Global Explainer =
- (Feature Permutation Importance)
- (Individual Conditional Expectation(ICE))
- (Partial Dependence Plot(PDP))
• Local Explainer =

ADS Global Explainer – Feature Permutation Importance
PassengerId Survived Pclass Name Sex Age SibSp Parch Fare Embarked
1 0 3 Braund, Mr. Owen male 22 1 0 7.25 S
2 1 1 Cumings, Mrs. John female 38 1 0 71.2833 C
3 1 3 Heikkinen, Miss. Laina female 26 0 0 7.925 S
4 1 1
Futrelle, Mrs. Jacques
Heath
female 35 1 0 53.1 S
1 0 3 Braund, Mr. Owen Female 22 1 0 7.25 S
2 1 1 Cumings, Mrs. John Male 38 1 0 71.2833 C
3 1 3 Heikkinen, Miss. Laina Male 26 0 0 7.925 S
4 1 1
Futrelle, Mrs. Jacques
Heath
male 35 1 0 53.1 S
(baseline_score) (shuffled_score)
baseline_score shuffled_score
baseline_score shuffled_score
•
•
baseline_score - shffuled_score

# With ADSExplainer, create a global explanation object using
# the MLXGlobalExplainer provider
from ads.explanations.mlx_global_explainer import MLXGlobalExplainer
global_explainer = explainer.global_explanation(
provider=MLXGlobalExplainer())
# A summary of the global feature permutation importance algorithm and
# how to interpret the output can be displayed with
global_explainer.feature_importance_summary()
# Compute the global Feature Permutation Importance explanation
importances = global_explainer.compute_feature_importance()
# ADS supports multiple visualizations for the global Feature
# Permutation Importance explanations (see "Interpretation" above)
# Simple bar chart highlighting the average impact on model score
# across multiple iterations of the algorithm
importances.show_in_notebook()
# Build the model using AutoML. 'model' is a subclass of type ADSModel.
# Note that the ADSExplainer below works with any model (classifier or
# regressor) that is wrapped in an ADSModel
import logging
from ads.automl.provider import OracleAutoMLProvider
from ads.automl.driver import AutoML
model, baseline = oracle_automl.train()
# Create the ADS explainer object, which is used to construct global
# and local explanation objects. The ADSExplainer takes as input the
# model to explain and the train/test dataset
from ads.explanations.explainer import ADSExplainer
explainer = ADSExplainer(test, model, training_data=train)
Global Explainer – Feature Importance Sample Code

ADS Global Explainer - Individual Conditional Expectation(ICE)
F1 F2 F3 T
2 1.2 0 15.1
7 2.4 4 12.5
8 9.7 3 18.1
. ... ... 13.5
F1 F2 F3 T
2 1.2 0 15.1
F1 F2 F3 T
1 1.2 0 ?
2 2.4 4 ?
3 9.7 3 ?
. ... ... ?
F1 F2 F3 T
1 1.2 0 13.5
2 2.4 4 15.1
3 9.7 3 17.5
. ... ... ...
F1
T
F1
input
T
( )
T
F1
F1 T
Oracle

ADS Global Explainer - Partial Dependence Plot(PDP)
F1 F2 F3 T
2 1.2 0 15.1
7 2.4 4 12.5
8 9.7 3 18.1
. ... ... 13.5
F1 F2 F3 T
2 1.2 0 15.1
F1 F2 F3 T
1 1.2 0 ?
2 2.4 4 ?
3 9.7 3 ?
. ... ... ?
F1 F2 F3 T
1 1.2 0 13.5
2 2.4 4 15.1
3 9.7 3 17.5
. ... ... ...
F1
T
ICE
ICE
PDP = ICE
( )
Oracle
ICE

from ads.explanations.mlx_global_explainer import MLXGlobalExplainer
global_explainer = explainer.global_explanation(
provider=MLXGlobalExplainer())
# A summary of the global partial feature dependence explanation
# algorithm and how to interpret the output can be displayed with
global_explainer.partial_dependence_summary()
# Compute the 1-feature PDP on the categorical feature, "sex",
# and numerical feature, "age"
pdp_sex = global_explainer.compute_partial_dependence("sex")
pdp_age = global_explainer.compute_partial_dependence(
"age", partial_range=(0, 1))
# ADS supports PDP visualizations for both 1-feature and 2-feature
# Feature Dependence explanations, and ICE visualizations for 1-feature
# Feature Dependence explanations (see "Interpretation" above)
# Visualize the categorical feature PDP for the True (Survived) label
pdp_sex.show_in_notebook(labels=True)
import logging
# Create the ADS explainer object, which is used to construct
# global and local explanation objects. The ADSExplainer takes
# as input the model to explain and the train/test dataset
# With ADSExplainer, create a global explanation object using
# the MLXGlobalExplainer provider
Global Explainer – ICE/PDP Sample Code

Local Explainer
•
• ( α)
• (Survived= 0 or 1)
•
1 0 3 Braund, Mr. Owen male 22 1 0 7.25 S
2 1 1 Cumings, Mrs. John female 38 1 0 71.2833C
3 1 3
Heikkinen, Miss.
Laina
female 26 0 0 7.925 S
... ... ... ... ... ... ... ... ... ...
) (https://www.kaggle.com/c/titanic)
500 ? 1
Anna. Miss.
Bworn
female 36 1 0 71.283 C
500 1 1
Anna. Miss.
Bworn
female 36 1 0 71.283 C
Why?

Local Explainer
1 0 3
Braund, Mr.
Owen
male 22 1 0 7.25 S
2 1 1
Cumings, Mrs.
John
female 38 1 0 71.2833 C
3 1 3
Heikkinen, Miss.
Laina
female 26 0 0 7.925 S
... ... ... ... ... ... ... ... ... ...
Oracle
500 ? 1
Anna. Miss.
Bworn
female 36 1 0 71.283 C
Passenger ID = 500
Passenger ID = 500
Oracle MLX

Local Explainer
PassengerID 500
PassengerID 500
( )

from ads.explanations.mlx_local_explainer import MLXLocalExplainer
local_explainer = explainer.local_explanation(
provider=MLXLocalExplainer())
# A summary of the local explanation algorithm and how to interpret
# the output can be displayed with
local_explainer.summary()
# Select a specific sample (instance/row) to generate a local
# explanation for
sample = 14
# Compute the local explanation on our sample from the test set
explanation = local_explainer.explain(test.X.iloc[sample:sample+1],
test.y.iloc[sample:sample+1])
# Visualize the explanation for the label True (Survived). See
# the "Interpretation" section above for more information
explanation.show_in_notebook(labels=True)
# Build the model using AutoML. 'model' is a subclass of type ADSModel.
import logging
# Create the ADS explainer object, which is used to construct
# global and local explanation objects. The ADSExplainer takes
# as input the model to explain and the train/test dataset
# With ADSExplainer, create a local explanation object using
# the MLXLocalExplainer provider
Local Explainer

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Data Science Platform
• ADS ML
• scikit-learn, keras, xgboost, lightGBM
scikit-learn lightGBM
OCI [ ]> [ ]
Notebook

Oracle Functions
OCI Data Science
OCI
API Gateway
http://hoge:8080/invoke/..
RESTEndpoint
OCI
Functions Service
OCI
Registry Service
Application
func.yml
func.py
scorefn.py
requirement.txt
?
cURL
•
•
• func.yml
• func.py
• scorefn.py
• requirement.txt
• ( )
• Fn OCI Functions
• OCI API Gateway
•
OCI (OCI
Functions)
• REST
(API
Gateway)
•
OCI
• REST
OCI Functions

20200402 oracle cloud infrastructure data science

20200402 oracle cloud infrastructure data science

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