Welcome to Dynamic Pipeline’s Documentation!¶

Dynamic Pipeline is a high-level API toolkit to help data scientists building models in ensemble way, and automating Machine Learning workflow with simple codes. Comparing other popular “AutoML or Automatic Machine Learning” APIs, Dynamic Pipeline is designed as an omni-ensembled ML workflow optimizer with higher-level API targeting to avoid manual repetitive train-along-evaluate experiments in general pipeline building.

To achieve that, Dynamic Pipeline creates Pipeline Cluster Traversal Experiments to assemble all cross-matching pipelines covering major tasks of Machine Learning workflow, and apply traversal-experiment to search the optimal baseline model. Besides, by modularizing all key pipeline components in reusable packages, it allows all components to be custom tunable along with high scalability.

The core concept in Dynamic Pipeline is Pipeline Cluster Traversal Experiments, which is a theory, first raised by Tony Dong during Genpact 2020 GVector Conference, to optimize and automate Machine Learning Workflow using ensemble pipelines algorithm.

Comparing other automatic or classic machine learning workflow’s repetitive experiments using single pipeline, Pipeline Cluster Traversal Experiments is more powerful, with larger coverage scope, to find the best model without manual intervention, and also more flexible with elasticity to cope with unseen data due to its ensemble designs in each component.

_images/PipelineClusterTraversalExperiments.PNG

In summary, Dynamic Pipeline shares a few useful properties for data scientists:

Easy & less coding - High-level APIs to implement Pipeline Cluster Traversal Experiments, and each ML component is highly automated and modularized;

Well ensembled - Each key component is ensemble of popular algorithms w/ optimal hyperparameters tuning included;

Hardly omission - Pipeline Cluster Traversal Experiments are designed to cross-experiment with combined permuted input datasets, feature selection, and model selection;

Scalable & Consistency - Each module could add new algorithms easily due to its ensemble & reusable design; no extra needs to modify existing codes for new experiment

Adaptable - Pipeline Cluster Traversal Experiments makes it easier to adapt unseen datasets with the right pipeline;

Custom Modify Welcomed - Support custom settings to add/remove algorithms or modify hyperparameters for elastic requirements.

Installation¶

To install Dynamic Pipeline, run this command in your terminal:

$ pip install dynapipe

This is the preferred method to install Dynamic Pipeline, as it will always install the most recent stable release.

autoPP Module¶

Description :

This module is used for data preprocessing operation:
- Impute with missing value
- Winsorize with outlier
- Scaling using popular scaler approaches
- Encoding category features using popular encoder approaches
- Generated all combination datasets for further modeling and evaluation
- Sparsity calculation as the critera for output datasets filtering
- Custom parameters initial settings, add/remove winsorization, scaling, or encoding strategies.
Class:
- dynaPreprocessing : Focus on classification/regression prprocessing problems
  
  fit() - fit & transform method for preprocessing
Current available strategies:
- Scaling : Numeric features scaling, default settings
  
  “standard” : StandardScaler() approach
  
  “minmax” - MinMaxScaler() approach
  
  “maxabs” - MaxAbsScaler() approach
  
  “robust” - RobustScaler() approach
- Encoding : Category features encoding, default settings
  
  “onehot” : OnehotEncoder() approach, with dummy trap consideration in regression problem
  
  “label” : LabelEncoder() approach
  
  “frequency” : Frequency calculation approach
  
  “mean” : Mean calculation approach
- winsorization : Default limits settings
  
  (0.01,0.01) : Top 1% and bottom 1% will be excluded
  
  (0.05,0.05) : Top 5% and bottom 5% will be excluded

dynapipePreprocessing¶

class dynapipe.autoPP.dynaPreprocessing(custom_parameters=None, label_col=None, model_type='reg', export_output_files=False)[source]¶

Automated feature preprocessing including imputation, winsorization, encoding, and scaling in ensemble algorithms, to generate permutation input datasets for further pipeline components.

Parameters:

custom_parameters (dictionary, default = None) –
Custom parameters settings input.

NOTE: default_parameters

= { “scaler” : [“None”, “standard”, “minmax”, “maxabs”, “robust”], “encode_band” : [10], “low_encode” : [“onehot”,”label”], “high_encode” : [“frequency”, “mean”], “winsorizer” : [(0.01,0.01),(0.05,0.05)], “sparsity” : [0.40], “cols” : [30] }
label_col (str, default = None) – Name of label column.
model_type (str, default = "reg") – “reg” for regression problem or “cls” for classification problem - Default: “reg”.
export_output_files (bool, default = False) – Export qualified permutated datasets to ./df_folder.

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#feature-preprocessing-for-a-regression-problem-using-autopp

References

None

fit(input_data=None)[source]¶

Fits and transforms a pandas dataframe to non-missing values, outlier excluded, categories encoded and scaled datasets by all algorithms permutation.

Parameters:	input_data (pandas dataframe, shape = [n_samples, n_features]) – NOTE: The input_data should be the datasets after basic data cleaning & well feature deduction, the more features involve will result in more columns permutation outputs.
Returns:	DICT_PREP_DF (dictionary) – Each key is the # of output preprocessed dataset, each value stores the dataset DICT_PREP_INFO (dictionary) – Dictionary for reference. Each key is the # of the output preprocessed dataset, each value stores the column names of the dataset NOTE - Log records will generate and save to ./logs folder automatedly.

PPtools¶

class dynapipe.funcPP.PPtools(data=None, label_col=None, model_type='reg')[source]¶

This class stores feature preprocessing transform tools.

Parameters:	data (df, default = None) – Pre-cleaned dataset for feature preprocessing. label_col (str, default = None) – Name of label column. model_type (str, default = "reg") – Value in [“reg”,”cls”]. The “reg” for regression problem, and “cls” for classification problem.

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#build-pipeline-cluster-traveral-experiments-using-autopipe

References

None

encode_tool(en_type=None, category_col=None)[source]¶

Category features encoding, included:: “onehot” - OneHot algorithm; “label” - LabelEncoder algorithm; “frequency” - Frequency Encoding algorithm; “mean” - Mean Encoding algorithm.

Parameters:	en_type (str, default = None) – Value in [“reg”,”cls”]. Will drop first encoded column to cope with dummy trap issue, when value is “reg”.
Returns:
Return type:	Encoded column/dataset for each category feature

impute_tool()[source]¶

Imputation with the missing values.

Parameters:	None –
Returns:
Return type:	None

remove_feature(feature_name)[source]¶

Remove feature.

Parameters:	feature_name (str/list, default = None) – column name, or list of column names wants to extract.
Returns:
Return type:	None

remove_zero_col_tool(data=None)[source]¶

Remove the columns with all value zero.

Parameters:	data (pandas dataset, default = None) – dataset needs to remove all zero columns
Returns:
Return type:	All-zero-columns dataset

scale_tool(df=None, sc_type=None)[source]¶

Feature scaling.

Parameters:	df (df, default = None) – Dataset wants to be scaled sc_type (str, default = None) – Value in [“None”,”standard”,”minmax”,”maxabs”,”robust”]. Select which scaling algorithm: “None” - No scale algorithm apply; “standard” - StandardScaler algorithm; “minmax” - MinMaxScaler algorithm; “maxabs” - MaxAbsScaler algorithm; “RobustScaler” - RobustScaler algorithm
Returns:
Return type:	Scaled dataset

sparsity_tool(data=None)[source]¶

Calculate the sparsity of the datset.

Parameters:	data (df, default = None) –
Returns:
Return type:	Value of sparsity

split_category_cols()[source]¶

Split input datasets to numeric dataset and category dataset.

Parameters:	None –
Returns:
Return type:	None

winsorize_tool(lower_ban=None, upper_ban=None)[source]¶

Feature outliers excluding with winsorization.

Parameters:	lower_ban (float, default = None) – Bottom percent of excluding data needs to set here. upper_ban – Top percent of excluding data needs to set here.
Returns:
Return type:	None

autoFS Module¶

Description :

This module is used for features selection:
- Automate the feature selection with several selectors
- Evaluate the outputs from all selector methods, and ranked a final list of the top important features
Class:
- dynaFS_clf : Focus on classification problems
  
  fit() - fit and transform method for classifier
- dynaFS_reg : Focus on regression problems
  
  fit() - fit and transform method for regressor
Current available selectors
- clf_fs : Class focusing on classification features selection
  
  kbest_f : SelectKBest() with f_classif core
  
  kbest_chi2 - SelectKBest() with chi2 core
  
  rfe_lr - RFE with LogisticRegression() estimator
  
  rfe_svm - RFE with SVC() estimator
  
  rfecv_svm - RFECV with SVC() estimator
  
  rfe_tree - RFE with DecisionTreeClassifier() estimator
  
  rfecv_tree - RFECV with DecisionTreeClassifier() estimator
  
  rfe_rf - RFE with RandomForestClassifier() estimator
  
  rfecv_rf - RFECV with RandomForestClassifier() estimator
- reg_fs : Class focusing on regression features selection
  
  kbest_f : SelectKBest() with f_regression core
  
  rfe_svm : RFE with SVC() estimator
  
  rfecv_svm : RFECV with SVC() estimator
  
  rfe_tree : RFE with DecisionTreeRegressor() estimator
  
  rfecv_tree : RFECV with DecisionTreeRegressor() estimator
  
  rfe_rf : RFE with RandomForestRegressor() estimator
  
  rfecv_rf : RFECV with RandomForestRegressor() estimator

dynaFS_clf¶

class dynapipe.autoFS.dynaFS_clf(fs_num=None, random_state=None, cv=None, in_pipeline=False, input_from_file=True)[source]¶

This class implements feature selection for classification problem.

Parameters:

fs_num (int, default = None) – Set the # of features want to select out.
random_state (int, default = None) – Random state value.
cv (int, default = None) – # of folds for cross-validation.
in_pipeline (bool, default = False) – Should be set to “True” when using autoPipe module to build Pipeline Cluster Traveral Experiments.
input_from_file (bool, default = True) – When input dataset is dataframe, needs to set “True”; Otherwise, i.e. array, needs to set “False”.

Example

References

None

fit(tr_features, tr_labels)[source]¶

Fits and transforms a dataframe with built-in algorithms, to select top features.

Parameters:

tr_features (df, default = None) – Train features columns. (NOTE: In the Pipeline Cluster Traversal Experiments, the features columns should be from the same pipeline dataset).
tr_labels (array/df, default = None) – Train label column, when input_from_file = True, must be pandas datframe. (NOTE: In the Pipeline Cluster Traversal Experiments, the label column should be from the same pipeline dataset).

Returns:

fs_num (int) – # of top features has been select out.
fs_results (array) – Selected & ranked top feature names.
NOTE - Log records will generate and save to ./logs folder automatedly.

dynaFS_reg¶

class dynapipe.autoFS.dynaFS_reg(fs_num=None, random_state=None, cv=None, in_pipeline=False, input_from_file=True)[source]¶

This class implements feature selection for regression problem.

Parameters:

fs_num (int, default = None) – Set the # of features want to select out.
random_state (int, default = None) – Random state value.
cv (int, default = None) – # of folds for cross-validation.
in_pipeline (bool, default = False) – Should be set to “True” when using autoPipe module to build Pipeline Cluster Traveral Experiments.
input_from_file (bool, default = True) – When input dataset is dataframe, needs to set “True”; Otherwise, i.e. array, needs to set “False”.

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#features-selection-for-a-regression-problem-using-autoFS

References

None

fit(tr_features, tr_labels)[source]¶

Fits and transforms a dataframe with built-in algorithms, to select top features.

Parameters:

tr_features (df, default = None) – Train features columns. (NOTE: In the Pipeline Cluster Traversal Experiments, the features columns should be from the same pipeline dataset).
tr_labels (array/df, default = None) – Train label column, when input_from_file = True, must be pandas datframe. (NOTE: In the Pipeline Cluster Traversal Experiments, the label column should be from the same pipeline dataset).

Returns:

fs_num (int) – # of top features has been select out.
fs_results (array) – Selected & ranked top feature names.
NOTE - Log records will generate and save to ./logs folder automatedly.

clf_fs¶

class dynapipe.selectorFS.clf_fs(fs_num=None, random_state=None, cv=None)[source]¶

This class stores classification selectors.

Parameters:	fs_num (int, default = None) – Set the # of features want to select out. random_state (int, default = None) – Random state value. cv (int, default = None) – # of folds for cross-validation.

Example

[Example]

References

None

reg_fs¶

class dynapipe.selectorFS.reg_fs(fs_num, random_state=None, cv=None)[source]¶

This class stores regression selectors.

Parameters:	fs_num (int, default = None) – Set the # of features want to select out. random_state (int, default = None) – Random state value. cv (int, default = None) – # of folds for cross-validation.

Example

[Example]

References

None

autoCV Module¶

Description :

This module is used for model selection:
- Automate the models training with cross validation
- GridSearch the best parameters
- Export the optimized models as pkl files, and saved them in /pkl folders
- Validate the optimized models, and select the best model

Class
- dynaClassifier : Focus on classification problems
  
  fit() : fit method for classifier
- dynaRegressor : Focus on regression problems
  
  fit() : fit method for regressor

Current available estimators

clf_cv : Class focusing on classification estimators

lgr - Logistic Regression (aka logit, MaxEnt) classifier - LogisticRegression()

svm : C-Support Vector Classification - SVM.SVC()

mlp : Multi-layer Perceptron classifier - MLPClassifier()

ada : An AdaBoost classifier - AdaBoostClassifier()

rf : Random Forest classifier - RandomForestClassifier()

gb : Gradient Boost classifier - GradientBoostingClassifier()

xgb : XGBoost classifier - xgb.XGBClassifier()

reg_cv : Class focusing on regression estimators

lr : Linear Regression - LinearRegression()

knn : Regression based on k-nearest neighbors - KNeighborsRegressor()

svr : Epsilon-Support Vector Regression - SVM.SVR()

rf : Random Forest Regression - RandomForestRegressor()

ada : An AdaBoost regressor - AdaBoostRegressor()

gb : Gradient Boosting for regression -GradientBoostingRegressor()

tree : A decision tree regressor - DecisionTreeRegressor()

mlp : Multi-layer Perceptron regressor - MLPRegressor()

xgb : XGBoost regression - XGBRegressor()

hgboost : Hist Gradient Boosting regression - HistGradientBoostingRegressor(); New added on 8/7/2020

huber : Huber regression - HuberRegressor(); New added on 8/7/2020

rgcv : Ridge cross validation regression - RidgeCV(); New added on 8/7/2020

cvlasso : Lasso cross validation regression - LassoCV(); New added on 8/7/2020

sgd : Stochastic Gradient Descent regression - SGDRegressor(); New added on 8/7/2020

dynaClassifier¶

class dynapipe.autoCV.dynaClassifier(custom_estimators=None, random_state=13, cv_num=5, in_pipeline=False, input_from_file=True)[source]¶

This class implements classification model selection with hyperparameters grid search and cross-validation.

Parameters:

custom_estimators (list, default = None) – Custom set the estimators in the autoCV regression module(if set None, will use all available estimators). Current version’s default available estimators are [‘lgr’,’svm’,’mlp’,’rf’,’ada’,’gb’,’xgb’].
random_state (int, default = None) – Random state value.
cv (int, default = None) – # of folds for cross-validation.
in_pipeline (bool, default = False) – Should be set to “True” when using autoPipe module to build Pipeline Cluster Traveral Experiments.
input_from_file (bool, default = True) – When input dataset is df, needs to set “True”; Otherwise, i.e. array, needs to set “False”.

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#model-selection-for-a-classification-problem-using-autocv

References

None

fit(tr_features=None, tr_labels=None)[source]¶

Fit and train datasets with classification hyperparameters GridSearch and CV across multiple estimators. Module will Auto save trained model as {estimator_name}_clf_model.pkl file to ./pkl folder. :param features: Train features columns. ( NOTE: In the Pipeline Cluster Traversal Experiments, the features columns should be from the same pipeline dataset). :type features: df, default = None :param labels: Train label column.

( NOTE: In the Pipeline Cluster Traversal Experiments, the label column should be from the same pipeline dataset).

Returns:	cv_num (int) – # of fold for cross-validation. DICT_EST (dictionary) – key is the name of estimators, value is the ralated trained model NOTE - Trained model auto save function only avalable when in_pipeline = “False”. NOTE - Log records will generate and save to ./logs folder automatedly.

dynaRegressor¶

class dynapipe.autoCV.dynaRegressor(custom_estimators=None, random_state=25, cv_num=5, in_pipeline=False, input_from_file=True)[source]¶

This class implements regression model selection with with hyperparameters grid search and cross-validation. Module will Auto save trained model as {estimator_name}_reg_model.pkl file to ./pkl folder.

Parameters:

custom_estimators (list, default = None) – Custom set the estimators in the autoCV regression module(if set None, will use all available estimators). Current version’s default available estimators are [‘lr’,’knn’,’tree’,’svm’,’mlp’,’rf’,’gb’,’ada’,’xgb’,’hgboost’,’huber’,’rgcv’,’cvlasso’,’sgd’].
random_state (int, default = None) – Random state value.
cv (int, default = None) – # of folds for cross-validation.
in_pipeline (bool, default = False) – Should be set to “True” when using autoPipe module to build Pipeline Cluster Traveral Experiments.
input_from_file (bool, default = True) – When input dataset is df, needs to set “True”; Otherwise, i.e. array, needs to set “False”.

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#model-selection-for-a-regression-problem-using-autocv

References

None

fit(tr_features=None, tr_labels=None)[source]¶

Fit and train datasets with regression hyperparameters GridSearch and CV across multiple estimators.

Parameters:

features (df, default = None) – Train features columns. ( NOTE: In the Pipeline Cluster Traversal Experiments, the features columns should be from the same pipeline dataset).
labels (df ,default = None) – Train label column. ( NOTE: In the Pipeline Cluster Traversal Experiments, the label column should be from the same pipeline dataset).

Returns:

cv_num (int) – # of fold for cross-validation.
DICT_EST (dictionary) – key is the name of estimators, value is the ralated trained model.
NOTE - Trained model auto save function only avalable when in_pipeline = “False”.
NOTE - Log records will generate and save to ./logs folder automatedly.

evaluate_model¶

class dynapipe.autoCV.evaluate_model(model_type=None, in_pipeline=False)[source]¶

This class implements model evaluation and return key score results.

Parameters:	model_type (str, default = None) – Value in [“reg”,”cls”]. The “reg” for regression problem, and “cls” for classification problem. in_pipeline (bool, default = False) – Should be set to “True” when using autoPipe module to build Pipeline Cluster Traveral Experiments.

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#build-pipeline-cluster-traveral-experiments-using-autopipe

References

fit(name=None, model=None, features=None, labels=None)[source]¶

Model evaluation with all models by the validate datasets.

Parameters:	name (str, default = None) – Estimator name. model (pkl, default = None) – Model needs to evaluate. Needs pkl file as input when in_pipeline = “False”; otherwise, should use DICT_EST[estimator name] as the input here. features (df, default = None) – Validate features columns. ( NOTE: In the Pipeline Cluster Traversal Experiments, the features columns should be from the same pipeline dataset). labels (df ,default = None) – Validate label column. ( NOTE: In the Pipeline Cluster Traversal Experiments, the label column should be from the same pipeline dataset).
Returns:	optimal_scores – When model_type = “cls”, will return [name,accuracy,precision,recall,latency] info of model validation results. when model_type = “reg”, will return [name,R-squared,MAE,MSE,RMSE,latency] info of model validation results.
Return type:	list

clf_cv¶

class dynapipe.estimatorCV.clf_cv(cv_val=None, random_state=None)[source]¶

This class stores classification estimators.

Parameters:	random_state (int, default = None) – Random state value. cv_val (int, default = None) – # of folds for cross-validation.

Example

[Example]

References

None

reg_cv¶

class dynapipe.estimatorCV.reg_cv(cv_val=None, random_state=None)[source]¶

This class stores regression estimators.

Parameters:	random_state (int, default = None) – Random state value. cv_val (int, default = None) – # of folds for cross-validation.

Example

[Example]

References

None

data_splitting_tool¶

dynapipe.utilis_func.data_splitting_tool(feature_cols=None, label_col=None, val_size=0.2, test_size=0.2, random_state=13)[source]¶

Splitting each pipeline’s dataset into train, validate, and test parts for Pipeline Cluster Traversal Experiments.

NOTE: When in_pipeline = “True”, this function will be built-in function in autoPipe module. So it needs to use pipeline_splitting_rule() to setup splitting rule.

Parameters:

label_col (array/df, default = None) – Column of label.
feature_cols (df, default = None) – Feature columns.
val_size (float, default = 0.2) – Value within [0~1]. Percentage of validate data. NOTE - When val_size with no input value will not return X_val & y_val
test_size (float, default = 0.2) – Value within [0~1]. Percentage of test data.
random_state (int, default = 13) – Random state value.

Returns:

X_train (array) – Train features dataset
y_train (array) – Train label dataset
X_val (array) – Validate features datset
y_val (array) – Validate label dataset
X_test (array) – Test features dataset
y_test (array) – Test label dataset

reset_parameters¶

dynapipe.utilis_func.reset_parameters()[source]¶

Reset autoCV estimators hyperparameters and searching range to default values.

Parameters:	None –
Returns:
Return type:	None

Example

update_parameters¶

dynapipe.utilis_func.update_parameters(mode='None', estimator_name='None', **kwargs)[source]¶

Update autoCV estimators hyperparameters and searching range to custom values.

NOTE: One line of command could only update one estimator.

Parameters:	mode (str, default = None) – Value in [“cls”,”reg”]. “cls” will modify classification estimators; “reg” will modify regression estimators. estimator_name (str, default = None) – Name of estimator. *kwargs (list, default = None*) – Lists of values using comma splitting, i.e. C=[0.1,0.2],kernel=[“linear”].
Returns:
Return type:	None

Example

export_parameters¶

dynapipe.utilis_func.export_parameters()[source]¶

Export current autoCV estimators hyperparameters and searching range to current work dictionary.

Parameters:	None –
Returns:
Return type:	None

Example

Defaults Parameters for Classifiers/Regressors¶

Estimators default parameters setting:

Classifiers Estimators Default Parameters Searching Range¶
Estimators:	Parameters:	Value Range:
lgr	‘C’	[0.001, 0.01, 0.1, 1, 10, 100, 1000]
svm	‘C’	[0.1, 1, 10]
	‘kernel’	[‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’]
mlp	‘activation’	[‘identity’,’relu’, ‘tanh’, ‘logistic’]
	‘hidden_layer_sizes’	[10, 50, 100]
	‘learning_rate’	[‘constant’, ‘invscaling’, ‘adaptive’]
	‘solver’	[‘lbfgs’, ‘sgd’, ‘adam’]
ada	‘n_estimators’	[50,100,150]
	‘learning_rate’	[0.01,0.1, 1, 5, 10]
rf	‘max_depth’	[2, 4, 8, 16, 32]
	‘n_estimators’	[5, 50, 250]
gb	‘n_estimators’	[50,100,150,200,250,300]
	‘max_depth’	[1, 3, 5, 7, 9]
	‘learning_rate’	[0.01, 0.1, 1, 10, 100]
xgb	‘n_estimators’	[50,100,150,200,250,300]
	‘max_depth’	[3, 5, 7, 9]
	‘learning_rate’	[0.01, 0.1, 0.2,0.3,0.4]

Regressors Default Parameters Searching Range¶
Estimators:	Parameters:	Value Range:
lr	‘normalize’	[True,False]
svm	‘C’	[0.1, 1, 10]
	‘kernel’	[‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’]
mlp	‘activation’	[‘identity’,’relu’, ‘tanh’, ‘logistic’]
	‘hidden_layer_sizes’	[10, 50, 100]
	‘learning_rate’	[‘constant’, ‘invscaling’, ‘adaptive’]
	‘solver’	[‘lbfgs’, ‘adam’]
ada	‘n_estimators’	[50,100,150,200,250,300]
	‘loss’	[‘linear’,’square’,’exponential’]
	‘learning_rate’	[0.01, 0.1, 0.2,0.3,0.4]
tree	‘splitter’	[‘best’, ‘random’]
	‘max_depth’	[1, 3, 5, 7, 9]
	‘min_samples_leaf’	[1,3,5]
rf	‘max_depth’	[2, 4, 8, 16, 32]
	‘n_estimators’	[5, 50, 250]
gb	‘n_estimators’	[50,100,150,200,250,300]
	‘max_depth’	[3, 5, 7, 9]
	‘learning_rate’	[0.01, 0.1, 0.2,0.3,0.4]
xgb	‘n_estimators’	[50,100,150,200,250,300]
	‘max_depth’	[3, 5, 7, 9]
	‘learning_rate’	[0.01, 0.1, 0.2,0.3,0.4]
sgd	‘shuffle’	[True,False]
	‘penalty’	[‘l2’, ‘l1’, ‘elasticnet’]
	‘learning_rate’	[‘constant’,’optimal’,’invscaling’]
cvlasso	‘fit_intercept’	[True,False]
rgcv	‘fit_intercept’	[True,False]
huber	‘fit_intercept’	[True,False]
hgboost	‘max_depth’	[3, 5, 7, 9]
	‘learning_rate’	[0.1, 0.2,0.3,0.4]

autoPipe Module¶

Description :

This module is used to build Pipeline Cluster Traversal Experiments:
- Create sequential components of Pipeline Cluster Traversal Experiments
- Apply traversal experiments through pipeline cluster to find the best baseline model
- Generate comparable and parameter-tracable dictionaies and reports to support autoVIZ and autoFlow modules
Build Steps:
- autoPP - dynaPreprocessing() Class in autoPP module
- Datasets Splitting - pipeline_splitting_rule() Function in utilis_funs module
- autoFS - dynaFS_clf() or dynaFS_reg() Class in autoFS module
- autoCV - dynaClassifier() or dynaRegressor() Class in autoCV module
- Model Evaluate - evaluate_model() Class in autoCV module

autoPipe¶

class dynapipe.autoPipe.autoPipe(steps)[source]¶

This class is to build Pipeline Cluster Traversal Experiments.

Parameters:	steps (list, default = None) – List of (name, transform) tuples (implementing fit & transform) that are chained, in the order in which they are chained, with the last object a model evaluation function.

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#build-pipeline-cluster-traveral-experiments-using-autopipe

References

None

fit(data)[source]¶

Fits and transforms a chain of Dynamic Pipeline modules.

Parameters: input_data (pandas dataframe, shape = [n_samples, n_features]) – NOTE: The input_data should be the datasets after basic data cleaning & well feature deduction, the more features involve will result in more columns permutation outputs.

Returns:

DICT_PREP_INFO (dictionary) – Each key is the # of preprocessed dataset(“Dataset_xxx” format, i.e. “Dataset_10”), each value stores an info string about what transforms applied. i.e. DICT_PREPROCESSING[‘Dataset_0’] stores value “winsor_0-Scaler_None– Encoded Features:[‘diagnosis’, ‘Size_3’, ‘area_mean’]”, which means applied 1st mode of winsorization, none scaler applied, and the encoded columns names(shown the enconding approachs in the names)
DICT_FEATURE_SELECTION_INFO (dictionary) – Each key is the # of preprocessed dataset, each value stores the name of features selected after the autoFS module.
DICT_MODELS_EVALUATION (dictionary) – Each key is the # of preprocessed dataset, each value stores the model evaluation results with its validate dataset.
DICT_DATA (dictionary) – Each key is the # of preprocessed dataset, and first level sub-key is the type of splitted sets(including ‘DICT_Train’,’DICT_TEST’,and’DICT_Validate’). The second level sub-key is “X” for features and “y” for label, each value stores the datasets related to the keys(Pandas Dataframe format) i.e. DICT_DATA[‘Dataset_0’][‘DICT_TEST’][“X”] is the train features of Dataset_0’s test dataset
models_summary (Pandas Dataframe) – Model selection results ranking table among all composits of preprocessed datasets, selected features and all posible models with optimal parameters.
NOTE - Log records will generate and save to ./logs folder automatedly.

autoViz Module¶

Description :

This module is used for outputs visualization:
- Visualize and retrieve each pipeline’s generating steps & performance;
- Using this module as the open interface for further interactive visualization development.

autoViz¶

class dynapipe.autoViz.autoViz(preprocess_dict=None, report=None)[source]¶

This class implements model visualization.

Parameters:	preprocess_dict (dict, default = None) – 1st output result (DICT_PREPROCESS) of autoPipe module. report (df, default = None) – 4th output result (dyna_report) of autoPipe module.

Example

References

clf_model_retrieval(metrics=None)[source]¶

This function implements classification model retrieval visualization.

Parameters:	metrics (str, default = None) – Value in [“accuracy”,”precision”,”recall”].

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#pipeline-cluster-traversal-experiments-model-retrieval-diagram-using-autoviz

References

autoFlow Module¶

Description :

This module is used for logging & model tracking:
- Log and retrieve each module’s operation history & intermediate results;
- Using this module as the open interface for further interactive model tracking development.

Here’re samples of each module’s log file:

Examples¶

Feature preprocessing for a regression problem using autoPP:¶

Demo Code:

import pandas as pd
from funcPP import PPtools
from autoPP import dynaPreprocessing

df = pd.read_csv('../data/preprocessing/breast_cancer.csv')

custom_parameters = {
    "scaler" : ["None", "standard"],
     # threshold number of category dimension
    "encode_band" : [6],
     # low dimension encoding
    "low_encode" : ["onehot","label"],
     # high dimension encoding
    "high_encode" : ["frequency", "mean"],
    "winsorizer" : [(0.1,0.1)],
    "sparsity" : [0.4862],
    "cols" : [27]
   }
dyna = dynaPreprocessing(custom_parameters = custom_parameters, label_col = 'diagnosis', model_type = "reg")
dict_df = dyna.fit(input_data = df)
print(f"Total combinations: {len(dict_df.keys())}")
print(dict_df['winsor_0-Scaler_standard-Dataset_441'])

Output:

 Now in Progress - Data Preprocessing Ensemble Iteration: Estimate about 0
Total combinations: 64
     diagnosis    Size_3  area_mean  compactness_mean  concave points_mea
0            1  1.290564   1.151477          1.730765             1.63873
1            1 -1.423416   1.823311         -0.679975             0.53514
2            1 -0.066426   1.823311          1.163585             1.63873
3            1  1.290564  -1.011406          1.730765             1.63873
4            1 -0.066426   1.823311          0.548763             1.61037
..         ...       ...        ...               ...                  ..
281          0 -0.066426  -0.866487         -1.300016            -0.80503
282          1 -0.066426   1.657991          0.807396             1.30604
283          1 -0.066426   0.462408          1.624135             1.17625
284          0 -0.066426  -0.552378         -0.290663            -0.60750
285          0  1.290564  -0.649460         -1.300016            -1.25679
[286 rows x 26 columns]

Features selection for a regression problem using autoFS:¶

Demo Code:

import pandas as pd
from dynapipe.autoFS import dynaFS_reg

tr_features = pd.read_csv('./data/regression/train_features.csv')
tr_labels = pd.read_csv('./data/regression/train_labels.csv')

# Set input_form_file = False, when label values are array. Select 'True' from Pandas dataframe.
reg_fs_demo = dynaFS_reg( fs_num = 5,random_state = 13,cv = 5,input_from_file = True)
# Select detail_info = True, when you want to see the detail of the iteration
reg_fs_demo.fit(tr_features,tr_labels,detail_info = False)

Output:

*DynaPipe* autoFS Module ===> Selector kbest_f gets outputs: ['INDUS', 'NOX', 'RM', 'PTRATIO', 'LSTAT']
Progress: [###-----------------] 14.3%

*DynaPipe* autoFS Module ===> Selector rfe_svm gets outputs: ['CHAS', 'NOX', 'RM', 'PTRATIO', 'LSTAT']
Progress: [######--------------] 28.6%

*DynaPipe* autoFS Module ===> Selector rfe_tree gets outputs: ['CRIM', 'RM', 'DIS', 'TAX', 'LSTAT']
Progress: [#########-----------] 42.9%

*DynaPipe* autoFS Module ===> Selector rfe_rf gets outputs: ['CRIM', 'RM', 'DIS', 'PTRATIO', 'LSTAT']
Progress: [###########---------] 57.1%

*DynaPipe* autoFS Module ===> Selector rfecv_svm gets outputs: ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']
Progress: [##############------] 71.4%

*DynaPipe* autoFS Module ===> Selector rfecv_tree gets outputs: ['CRIM', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'TAX', 'PTRATIO', 'B', 'LSTAT']
Progress: [#################---] 85.7%

*DynaPipe* autoFS Module ===> Selector rfecv_rf gets outputs: ['CRIM', 'ZN', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']
Progress: [####################] 100.0%

The DynaPipe autoFS identify the top 5 important features for regression are: ['RM', 'LSTAT', 'PTRATIO', 'NOX', 'CRIM'].

Model selection for a classification problem using autoCV:¶

Demo Code:

import pandas as pd
from dynapipe.autoCV import dynaClassifier,evaluate_clf_model
import joblib

tr_features = pd.read_csv('./data/classification/train_features.csv')
tr_labels = pd.read_csv('./data/classification/train_labels.csv')
val_features = pd.read_csv('./data/classification/val_features.csv')
val_labels = pd.read_csv('./data/classification/val_labels.csv')

# Set input_form_file = False, when label values are array. Select 'True' from Pandas dataframe.
clf_cv_demo = dynaClassifier(random_state = 13,cv_num = 5,input_from_file = True)
# Select detail_info = True, when you want to see the detail of the iteration
clf_cv_demo.fit(tr_features,tr_labels,detail_info = False)

models = {}
for mdl in ['lgr','svm','mlp','rf','ada','gb','xgb']:
    models[mdl] = joblib.load('./pkl/{}_clf_model.pkl'.format(mdl))

for name, mdl in models.items():
    evaluate_clf_model(name, mdl, val_features, val_labels)

Output:

*DynaPipe* autoCV Module ===> lgr_CrossValidation with 5 folds:

Best Parameters: {'C': 1, 'random_state': 13}

Best CV Score: 0.7997178628107917

Progress: [###-----------------] 14.3%

*DynaPipe* autoCV Module ===> svm_CrossValidation with 5 folds:

Best Parameters: {'C': 0.1, 'kernel': 'linear'}

Best CV Score: 0.7959619114794568

Progress: [######--------------] 28.6%

*DynaPipe* autoCV Module ===> mlp_CrossValidation with 5 folds:

Best Parameters: {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant', 'random_state': 13, 'solver': 'lbfgs'}

Best CV Score: 0.8184094515958386

Progress: [#########-----------] 42.9%

*DynaPipe* autoCV Module ===> rf_CrossValidation with 5 folds:

Best Parameters: {'max_depth': 4, 'n_estimators': 250, 'random_state': 13}

Best CV Score: 0.8240521953800035

Progress: [###########---------] 57.1%

*DynaPipe* autoCV Module ===> ada_CrossValidation with 5 folds:

Best Parameters: {'learning_rate': 0.1, 'n_estimators': 100, 'random_state': 13}

Best CV Score: 0.824034561805678

Progress: [##############------] 71.4%

*DynaPipe* autoCV Module ===> gb_CrossValidation with 5 folds:

Best Parameters: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 300, 'random_state': 13}

Best CV Score: 0.8408746252865456

Progress: [#################---] 85.7%

*DynaPipe* autoCV Module ===> xgb_CrossValidation with 5 folds:

Best Parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 200, 'verbosity': 0}

Best CV Score: 0.8464292011990832

Progress: [####################] 100.0%

lgr -- Accuracy: 0.775 / Precision: 0.712 / Recall: 0.646 / Latency: 0.0ms
svm -- Accuracy: 0.747 / Precision: 0.672 / Recall: 0.6 / Latency: 2.0ms
mlp -- Accuracy: 0.787 / Precision: 0.745 / Recall: 0.631 / Latency: 4.1ms
rf -- Accuracy: 0.809 / Precision: 0.83 / Recall: 0.6 / Latency: 37.0ms
ada -- Accuracy: 0.792 / Precision: 0.759 / Recall: 0.631 / Latency: 21.4ms
gb -- Accuracy: 0.815 / Precision: 0.796 / Recall: 0.662 / Latency: 2.0ms
xgb -- Accuracy: 0.815 / Precision: 0.786 / Recall: 0.677 / Latency: 5.0ms

Model selection for a regression problem using autoCV:¶

Demo Code:

import pandas as pd
from dynapipe.autoCV import evaluate_model,dynaClassifier,dynaRegressor
import joblib

from dynapipe.utilis_func import pipeline_splitting_rule, update_parameters,reset_parameters
reset_parameters()

tr_features = pd.read_csv('./data/regression/train_features.csv')
tr_labels = pd.read_csv('./data/regression/train_labels.csv')
val_features = pd.read_csv('./data/regression/val_features.csv')
val_labels = pd.read_csv('./data/regression/val_labels.csv')
te_features = pd.read_csv('./data/regression/test_features.csv')
te_labels = pd.read_csv('./data/regression/test_labels.csv')

reg_cv_demo = dynaRegressor(random_state=13,cv_num = 5)

reg_cv_demo.fit(tr_features,tr_labels)

models = {}

for mdl in ['lr','knn','tree','svm','mlp','rf','gb','ada','xgb','hgboost','huber','rgcv','cvlasso','sgd']:
    models[mdl] = joblib.load('./pkl/{}_reg_model.pkl'.format(mdl))

for name, mdl in models.items():
    try:
        ml_evl = evaluate_model(model_type = "reg")
        ml_evl.fit(name, mdl, val_features, val_labels)
    except:
        print(f"Failed to load the {mdl}.")

Output:

Done with the parameters reset.
Now in Progress - Model Selection w/ Cross-validation: Estimate about 0.0337 minutes left  [#-------------------] 7.1%

    *DynaPipe* autoCV Module ===> lr model CrossValidation with 5 folds:
Best Parameters: {'normalize': False}

Best CV Score: 0.682929422892965

Now in Progress - Model Selection w/ Cross-validation: Estimate about 0.5549 minutes left  [###-----------------] 14.3%

    *DynaPipe* autoCV Module ===> knn model CrossValidation with 5 folds:
Best Parameters: {'algorithm': 'auto', 'n_neighbors': 10, 'weights': 'distance'}

Best CV Score: 0.5277324478219082

Now in Progress - Model Selection w/ Cross-validation: Estimate about 0.2383 minutes left  [####----------------] 21.4%

    *DynaPipe* autoCV Module ===> tree model CrossValidation with 5 folds:
Best Parameters: {'max_depth': 5, 'min_samples_leaf': 3, 'splitter': 'best'}

Best CV Score: 0.7704058399460141

Now in Progress - Model Selection w/ Cross-validation: Estimate about 11.0461 minutes left  [######--------------] 28.6%

    *DynaPipe* autoCV Module ===> svm model CrossValidation with 5 folds:
Best Parameters: {'C': 1, 'kernel': 'linear'}

Best CV Score: 0.6817778239200576

Now in Progress - Model Selection w/ Cross-validation: Estimate about 20.2113 minutes left  [#######-------------] 35.7%

    *DynaPipe* autoCV Module ===> mlp model CrossValidation with 5 folds:
Best Parameters: {'activation': 'identity', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant', 'random_state': 13, 'solver': 'lbfgs'}

Best CV Score: 0.6556246414762388

Now in Progress - Model Selection w/ Cross-validation: Estimate about 3.1693 minutes left  [#########-----------] 42.9%

    *DynaPipe* autoCV Module ===> rf model CrossValidation with 5 folds:
Best Parameters: {'max_depth': 8, 'n_estimators': 50}

Best CV Score: 0.8582920563031621

Now in Progress - Model Selection w/ Cross-validation: Estimate about 18.0094 minutes left  [##########----------] 50.0%

    *DynaPipe* autoCV Module ===> gb model CrossValidation with 5 folds:
Best Parameters: {'learning_rate': 0.2, 'max_depth': 3, 'n_estimators': 100}

Best CV Score: 0.8794018441486111

Now in Progress - Model Selection w/ Cross-validation: Estimate about 18.7663 minutes left  [###########---------] 57.1%

    *DynaPipe* autoCV Module ===> ada model CrossValidation with 5 folds:
Best Parameters: {'learning_rate': 0.3, 'loss': 'linear', 'n_estimators': 150, 'random_state': 13}

Best CV Score: 0.8255039215809923

Now in Progress - Model Selection w/ Cross-validation: Estimate about 4.545 minutes left  [#############-------] 64.3%

    *DynaPipe* autoCV Module ===> xgb model CrossValidation with 5 folds:
Best Parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 300, 'verbosity': 0}

Best CV Score: 0.8645505523555148

Now in Progress - Model Selection w/ Cross-validation: Estimate about 1.6471 minutes left  [##############------] 71.4%

    *DynaPipe* autoCV Module ===> hgboost model CrossValidation with 5 folds:
Best Parameters: {'learning_rate': 0.2, 'max_depth': 3}

Best CV Score: 0.8490465745463796

Now in Progress - Model Selection w/ Cross-validation: Estimate about 0.0182 minutes left  [################----] 78.6%

    *DynaPipe* autoCV Module ===> huber model CrossValidation with 5 folds:
Best Parameters: {'fit_intercept': False}

Best CV Score: 0.6250877399211718

Now in Progress - Model Selection w/ Cross-validation: Estimate about 0.0024 minutes left  [#################---] 85.7%

    *DynaPipe* autoCV Module ===> rgcv model CrossValidation with 5 folds:
Best Parameters: {'fit_intercept': True}

Best CV Score: 0.6814764830347567

Now in Progress - Model Selection w/ Cross-validation: Estimate about 0.011 minutes left  [###################-] 92.9%

    *DynaPipe* autoCV Module ===> cvlasso model CrossValidation with 5 folds:
Best Parameters: {'fit_intercept': True}

Best CV Score: 0.6686184981380419

Now in Progress - Model Selection w/ Cross-validation: Estimate about 0.0 minutes left  [####################] 100.0%

    *DynaPipe* autoCV Module ===> sgd model CrossValidation with 5 folds:
Best Parameters: {'learning_rate': 'invscaling', 'penalty': 'elasticnet', 'shuffle': True}

Best CV Score: -1.445728757185719e+26

lr -- R^2 Score: 0.684 / Mean Absolute Error: 3.674 / Mean Squared Error: 24.037 / Root Mean Squared Error: 24.037 / Latency: 2.0s
knn -- R^2 Score: 0.307 / Mean Absolute Error: 4.639 / Mean Squared Error: 52.794 / Root Mean Squared Error: 52.794 / Latency: 3.0s
tree -- R^2 Score: 0.671 / Mean Absolute Error: 3.141 / Mean Squared Error: 25.077 / Root Mean Squared Error: 25.077 / Latency: 1.0s
svm -- R^2 Score: 0.649 / Mean Absolute Error: 3.466 / Mean Squared Error: 26.746 / Root Mean Squared Error: 26.746 / Latency: 7.0s
mlp -- R^2 Score: 0.629 / Mean Absolute Error: 3.56 / Mean Squared Error: 28.244 / Root Mean Squared Error: 28.244 / Latency: 4.0s
rf -- R^2 Score: 0.772 / Mean Absolute Error: 2.677 / Mean Squared Error: 17.327 / Root Mean Squared Error: 17.327 / Latency: 10.0s
gb -- R^2 Score: 0.775 / Mean Absolute Error: 2.616 / Mean Squared Error: 17.126 / Root Mean Squared Error: 17.126 / Latency: 1.0s
ada -- R^2 Score: 0.749 / Mean Absolute Error: 2.933 / Mean Squared Error: 19.09 / Root Mean Squared Error: 19.09 / Latency: 18.0s
xgb -- R^2 Score: 0.776 / Mean Absolute Error: 2.66 / Mean Squared Error: 17.02 / Root Mean Squared Error: 17.02 / Latency: 5.0s
hgboost -- R^2 Score: 0.758 / Mean Absolute Error: 2.98 / Mean Squared Error: 18.412 / Root Mean Squared Error: 18.412 / Latency: 9.2s
huber -- R^2 Score: 0.613 / Mean Absolute Error: 3.63 / Mean Squared Error: 29.476 / Root Mean Squared Error: 29.476 / Latency: 4.0s
rgcv -- R^2 Score: 0.672 / Mean Absolute Error: 3.757 / Mean Squared Error: 24.983 / Root Mean Squared Error: 24.983 / Latency: 3.0s
cvlasso -- R^2 Score: 0.661 / Mean Absolute Error: 3.741 / Mean Squared Error: 25.821 / Root Mean Squared Error: 25.821 / Latency: 4.0s
sgd -- R^2 Score: -7.6819521340367e+26 / Mean Absolute Error: 239048363331832.62 / Mean Squared Error: 5.849722584020232e+28 / Root Mean Squared Error: 5.849722584020232e+28 / Latency: 1.0s

Custom estimators & parameters setting for for autoCV:¶

Currently, there’re 3 methods in utilis_fun module - reset_parameters, update_parameters, and export_parameters.

update_parameters method is used to modify the default parameter settings for models selection module (autoCV).

i.e. When you want to modify the support vector machine classifier, with new penalty “C” and “kernel” values, the code line below will achieve that.

update_parameters(mode = "cls", estimator_name = "svm", C=[0.1,0.2],kernel=["linear"])

export_parameters method can help you export the currnt default parameter settings as 2 csv files named “exported_cls_parameters.csv” and “exported_reg_parameters.csv”. You can find them in the ./exported folder of you current work dictionary.

export_parameters()

reset_parameters method can reset the default parameter settings to the package’s original default settings. Just add this code line will work:

reset_parameters()

Build Pipeline Cluster Traveral Experiments using autoPipe:¶

Demo Code:

import pandas as pd
from dynapipe.autoPipe import autoPipe
from dynapipe.funcPP import PPtools
from dynapipe.autoPP import dynaPreprocessing
from dynapipe.autoFS import dynaFS_clf
from dynapipe.autoCV import evaluate_model,dynaClassifier

df = pd.read_csv('./data/preprocessing/breast_cancer.csv')

pipe = autoPipe(
[("autoPP",dynaPreprocessing(custom_parameters = None, label_col = 'diagnosis', model_type = "cls")),
("datasets_splitting",pipeline_splitting_rule(val_size = 0.2, test_size = 0.2, random_state = 13)),
("autoFS",dynaFS_clf(fs_num = 5, random_state=13, cv = 5, in_pipeline = True, input_from_file = False)),
("autoCV",dynaClassifier(random_state = 13,cv_num = 5,in_pipeline = True, input_from_file = False)),
("model_evaluate",evaluate_model(model_type = "cls"))])

dyna_report= pipe.fit(df)[4]
dyna_report.head(5)

Output:

      Dataset    Model_Name    Best_Parameters         Accuracy       Precision       Recall  Latency
   Dataset_0       svm     [('C', 0.1), ('kernel', 'linear')]        0.930 0.889 0.96 3.0
   Dataset_0       xgb     [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]    0.912   0.955   0.84    2.0
  Dataset_5       gb      [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]    0.895   0.913   0.84    2.0
  Dataset_4       rf      [('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]  0.877   0.821   0.92    12.0
  Dataset_7       mlp     [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] 0.772   0.875   0.56    4.0

Pipeline Cluster Traversal Experiments Model Retrieval Diagram using autoViz:¶

Demo Code:

from dynapipe.autoViz import autoViz
viz = autoViz(preprocess_dict=DICT_PREPROCESSING,report=dyna_report)
viz.clf_model_retrieval(metrics='accuracy')

Output:

Contributers¶

Original Author¶

Tony Dong <https://www.linkedin.com/in/lei-tony-dong/>

Contributors¶

Send email to <tonyleidong@gmail.com> to be one of us.

History¶

0.2.2 (2020-8-7)¶

Add estimators: HuberRegressor, RidgeCV, LassoCV, SGDRegressor, and HistGradientBoostingRegressor
Modify parameters.json, and reset_parameters.json for the added estimators
Add autoViz for classification problem model retrieval diagram

0.2.1 (2020-7-31)¶

Bug fix and stable version with pipeline modules

0.1.4 (2020-7-30)¶

Add autoPP & autoPipe modules

0.1.3 (2020-07-21)¶

Add online documentation page: https://dynamic-pipeline.readthedocs.io

0.1.0 (2020-07-21)¶

Debug in the estimatorCV.py

0.0.11 (2020-07-21)¶

Add JSON file as the repository for the estimators’ parameters
Added functions reset_parameters(), export_parameters(), and reset_parameters() to manipulate the default parameters for estimatorCV.py
Update the README.md file
Debug in the autoCV.py

0.0.8 (2020-07-18)¶

First release on PyPI.

Report Issues¶

Report Issues at https://github.com/tonyleidong/dynapipe/issues.

If you are reporting a bug, please include:

Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.

Welcome to Dynamic Pipeline’s Documentation!¶

Installation¶

autoPP Module¶

dynapipePreprocessing¶

PPtools¶

autoFS Module¶

dynaFS_clf¶

dynaFS_reg¶

clf_fs¶

reg_fs¶

autoCV Module¶

dynaClassifier¶

dynaRegressor¶

evaluate_model¶

clf_cv¶

reg_cv¶

data_splitting_tool¶

reset_parameters¶

update_parameters¶

export_parameters¶

Defaults Parameters for Classifiers/Regressors¶

autoPipe Module¶

autoPipe¶

autoViz Module¶

autoViz¶

autoFlow Module¶

Examples¶

Feature preprocessing for a regression problem using autoPP:¶

Features selection for a regression problem using autoFS:¶

Model selection for a classification problem using autoCV:¶

Model selection for a regression problem using autoCV:¶

Custom estimators & parameters setting for for autoCV:¶

Build Pipeline Cluster Traveral Experiments using autoPipe:¶

Pipeline Cluster Traversal Experiments Model Retrieval Diagram using autoViz:¶

Contributers¶

Original Author¶

Contributors¶

History¶

0.2.2 (2020-8-7)¶

0.2.1 (2020-7-31)¶

0.1.4 (2020-7-30)¶

0.1.3 (2020-07-21)¶

0.1.0 (2020-07-21)¶

0.0.11 (2020-07-21)¶

0.0.8 (2020-07-18)¶

Report Issues¶

Indices and tables¶