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Update demo project to use OmegaConfigLoader (#1590)
* Add missing dependency Signed-off-by: Alain Anghelidi <[email protected]> * Replace deprecated command in README Signed-off-by: Alain Anghelidi <[email protected]> * Bump kedro init version Signed-off-by: Alain Anghelidi <[email protected]> * Use OmegaConfigLoader in project settings Signed-off-by: Alain Anghelidi <[email protected]> * Remove unneeded CONFIG_LOADER_ARGS Signed-off-by: Alain Anghelidi <[email protected]> * Update configuration to use OmegaConfigLoader Signed-off-by: Alain Anghelidi <[email protected]> * Fix parameters of modeling pipeline Since the version 1.1 of scikit learn the default value of the parameter max_features of RandomForestRegressor have been changed from 'auto' to 1.0. Support for the old 'auto' value used have been removed. This commit fix this issue. Signed-off-by: Alain Anghelidi <[email protected]> * Fix Pandas SettingWithCopyWarning by using loc Signed-off-by: Alain Anghelidi <[email protected]> --------- Signed-off-by: Alain Anghelidi <[email protected]>
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@@ -1,27 +1,27 @@ | ||
ingestion.int_typed_companies: | ||
type: pandas.ParquetDataSet | ||
filepath: ${base_location}/02_intermediate/typed_companies.pq | ||
filepath: ${_base_location}/02_intermediate/typed_companies.pq | ||
metadata: | ||
kedro-viz: | ||
layer: intermediate | ||
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ingestion.int_typed_shuttles@pandas1: | ||
type: pandas.ParquetDataSet | ||
filepath: ${base_location}/02_intermediate/typed_shuttles.pq | ||
filepath: ${_base_location}/02_intermediate/typed_shuttles.pq | ||
metadata: | ||
kedro-viz: | ||
layer: intermediate | ||
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ingestion.int_typed_shuttles@pandas2: | ||
type: pandas.ParquetDataSet | ||
filepath: ${base_location}/02_intermediate/typed_shuttles.pq | ||
filepath: ${_base_location}/02_intermediate/typed_shuttles.pq | ||
metadata: | ||
kedro-viz: | ||
layer: intermediate | ||
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ingestion.int_typed_reviews: | ||
type: pandas.ParquetDataSet | ||
filepath: ${base_location}/02_intermediate/typed_reviews.pq | ||
filepath: ${_base_location}/02_intermediate/typed_reviews.pq | ||
metadata: | ||
kedro-viz: | ||
layer: intermediate |
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@@ -1,14 +1,13 @@ | ||
prm_shuttle_company_reviews: | ||
type: pandas.ParquetDataSet | ||
filepath: ${base_location}/03_primary/prm_shuttle_company_reviews.pq | ||
filepath: ${_base_location}/03_primary/prm_shuttle_company_reviews.pq | ||
metadata: | ||
kedro-viz: | ||
layer: primary | ||
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prm_spine_table: | ||
type: pandas.ParquetDataSet | ||
filepath: ${base_location}/03_primary/prm_spine_table.pq | ||
filepath: ${_base_location}/03_primary/prm_spine_table.pq | ||
metadata: | ||
kedro-viz: | ||
layer: primary | ||
|
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@@ -1,36 +1,12 @@ | ||
# Jinja is super powerful, but does come at the cost of readability | ||
# Set your IDE to Jinja YAML to ensure this is highlighted correctly | ||
# Use dataset factories to reduce duplication | ||
"feature_engineering.feat_{metric_type}_metrics": | ||
type: pandas.ParquetDataSet | ||
filepath: ${_base_location}/04_feature/feat_{metric_type}_metrics.pq | ||
layer: feature | ||
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{% set namespace = 'feature_engineering' %} | ||
{% set metric_types = ['weighting', 'scaling'] %} | ||
{% for metric_type in metric_types %} | ||
{{ namespace }}.feat_{{ metric_type }}_metrics: | ||
type: pandas.ParquetDataSet | ||
filepath: ${base_location}/04_feature/feat_{{ metric_type }}_metrics.pq | ||
metadata: | ||
kedro-viz: | ||
layer: feature | ||
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{% endfor %} | ||
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# This will render to generate the records below... | ||
# | ||
# feature_engineering.feat_weighting_metrics: | ||
# type: pandas.ParquetDataSet | ||
# filepath: ${base_location}/04_feature/feat_weighting_metrics.pq | ||
# layer: feature | ||
# | ||
# feature_engineering.feat_scaling_metrics: | ||
# type: pandas.ParquetDataSet | ||
# filepath: ${base_location}/04_feature/feat_scaling_metrics.pq | ||
# layer: feature | ||
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feature_importance_output: | ||
feature_importance_output: | ||
type: pandas.CSVDataSet | ||
filepath: ${base_location}/04_feature/feature_importance_output.csv | ||
filepath: ${_base_location}/04_feature/feature_importance_output.csv | ||
metadata: | ||
kedro-viz: | ||
layer: feature | ||
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layer: feature |
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@@ -1,7 +1,6 @@ | ||
model_input_table: | ||
type: pandas.ParquetDataSet | ||
filepath: ${base_location}/05_model_input/model_input_table.pq | ||
filepath: ${_base_location}/05_model_input/model_input_table.pq | ||
metadata: | ||
kedro-viz: | ||
layer: model_input | ||
|
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@@ -1,9 +1,9 @@ | ||
train_evaluation.linear_regression.regressor: | ||
type: pickle.PickleDataSet | ||
filepath: ${base_location}/06_models/linear_regression.pkl | ||
filepath: ${_base_location}/06_models/linear_regression.pkl | ||
versioned: True | ||
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train_evaluation.random_forest.regressor: | ||
type: pickle.PickleDataSet | ||
filepath: ${base_location}/06_models/random_forest.pkl | ||
filepath: ${_base_location}/06_models/random_forest.pkl | ||
versioned: True |
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@@ -1,19 +1,19 @@ | ||
train_evaluation.linear_regression.r2_score: | ||
type: tracking.MetricsDataSet | ||
filepath: ${base_location}/09_tracking/linear_score.json | ||
filepath: ${_base_location}/09_tracking/linear_score.json | ||
versioned: True | ||
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train_evaluation.random_forest.r2_score: | ||
type: tracking.MetricsDataSet | ||
filepath: ${base_location}/09_tracking/rf_score.json | ||
filepath: ${_base_location}/09_tracking/rf_score.json | ||
versioned: True | ||
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train_evaluation.linear_regression.experiment_params: | ||
type: tracking.JSONDataSet | ||
filepath: ${base_location}/09_tracking/linear_params.json | ||
filepath: ${_base_location}/09_tracking/linear_params.json | ||
versioned: True | ||
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train_evaluation.random_forest.experiment_params: | ||
type: tracking.JSONDataSet | ||
filepath: ${base_location}/09_tracking/rf_params.json | ||
filepath: ${_base_location}/09_tracking/rf_params.json | ||
versioned: True |
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_base_location: data/ |
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_base_location: s3://my_bucket/production/ |
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|
@@ -16,3 +16,4 @@ wheel>=0.35, <0.37 | |
pillow~=9.0 | ||
matplotlib==3.5.0 | ||
pre-commit~=1.17 | ||
seaborn~=0.11.2 |
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