Streamlined Machine Learning is a high level machine learning workflow wrapper built around sklearn and scipy.stats. There exist three primary functions: 1. Transformation Preprocessing 2. Model Selection 3. Feature Selection. streamml contains a set of robust functions built on top of the sklearn framework and aims at streamlining all of the processes in the context of a flow.
pip install -U streamml2
The three main classes in the streamml ecosystem are: TransformationStream, ModelSelectionStream, and FeatureSelectionStream. The underlying assumption before running any of these objects and their capabilities is that you have cleaned and completely preprocessed your data of all the nasty you would do before running any model or transformation in sklearn. All X and y data piped into these models must be a pandas.DataFrame, even your pandas.Series style y data (this simplifies and unifies functionality accross the ecosystem). That said, TransformationStream is constructed with X, then has the ability to flow through a cadre of different manifold, clustering, or transformation functions built into the ecosystem (which are explained the documentation in further detail). ModelSelectionStream is constructed with both X and y, where y can be categorical (binary or n-ary), then has the ability to flow through a cadre of different sklearn based model objects. The underlying assumption is that your y data has been categorized into a numeric representation, as this is how sklearn prefers it. We recommend you simply use pandas.factorize to accomplish this, but this is not done explicitely or implicitely for you. Lastly FeatureSelectionStream is constructed with both X and y, then has the ability to flow through a cadre of very specific types of model objects and ensemble functions. The models are ones in the sklearn packages that contain
coef_, feature_importance_, or p-values attributes produced after the hyper-tuning phase or running the model. As this is unintuitive at first, these include, but are not limited to: OLS p-values, Random Forest feature importance, or Lasso coefficients.
In the streamml ecosystem, as mentioned above, we must build a stream object. The idea is within this stream, we can flow through very specific objects that are optimized for us behind the scenes. Yup. That's it. All of the gridsearching and pipelining procedures you are use to doing everytime you see a dataset are already built in. Just construct a stream and then .flow([...]) right on through it, and it will return your hypertuned models, transformed feature-space, or a subspace of features that are most pronounced within your data.
Streaming Capabilities provided:
TransformationStream, meant to flow through preprocessing techniques such as: scaling, normalizing, boxcox, binarization, pca, or kmeans aimed at returning a desired input dataset for model development.ModelSelectionStream.Regression Models: {"lr" : linearRegression, "svr" : supportVectorRegression, "rfr":randomForestRegression, "abr":adaptiveBoostingRegression, "knnr":knnRegression, "ridge":ridgeRegression, "lasso":lassoRegression, "enet":elasticNetRegression, "mlpr":multilayerPerceptronRegression, "br":baggingRegression, "dtr":decisionTreeRegression, "gbr":gradientBoostingRegression, "gpr":gaussianProcessRegression, "hr":huberRegression, "tsr":theilSenRegression, "par":passiveAggressiveRegression, "ard":ardRegression, "bays_ridge":bayesianRidgeRegression, "lasso_lar":lassoLeastAngleRegression, "lar":leastAngleRegression}
Regression metrics: ['rmse','mse', 'r2','explained_variance','mean_absolute_error','median_absolute_error']
Classification Models:
{'abc':adaptiveBoostingClassifier, 'dtc':decisionTreeClassifier, 'gbc':gradientBoostingClassifier, 'gpc':guassianProcessClassifier, 'knnc':knnClassifier, 'logr':logisticRegressionClassifier, 'mlpc':multilayerPerceptronClassifier, 'nbc':naiveBayesClassifier, 'rfc':randomForestClassifier, 'sgd':stochasticGradientDescentClassifier, 'svc':supportVectorClassifier}Classification Metrics: ["auc","prec","recall","f1","accuracy", "kappa","log_loss"]
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FeatureSelectionStream, meant to flow through several predictive models and algorithms to determine which subset of features is most predictive or representative of your dataset, these include: RandomForestFeatureImportance, LassoFeatureImportance, MixedSelection, and a technique to ensemble each named TOPSISFeatureRanking. You must specify whether your wish to ensemble and with what technique (denotedensemble=True). This is not currently supported, however will be built on top the sklearn.feature_selection.
Supported stream operators: scale, normalize, boxcox, binarize, pca, kmeans, brbm (Bernoulli Restricted Boltzman Machine).
import pandas as pd
from streamml2.streams import TransformationStream
from sklearn.datasets import fetch_20newsgroups
categories = ['alt.atheism', 'talk.religion.misc','comp.graphics', 'sci.space']
newsgroups_train = fetch_20newsgroups(subset='train',categories=categories)
X2 = TransformationStream(newsgroups_train.data,corpus=True, method='tfidf').flow(["pca","normalize","kmeans"],
params={"pca__percent_variance":0.95,
"kmeans__n_clusters":len(categories)})
print(X2)
from sklearn.datasets import load_iris
iris=load_iris()
X=pd.DataFrame(iris['data'], columns=iris['feature_names'])
y=pd.DataFrame(iris['target'], columns=['target'])
X2 = TransformationStream(X).flow(["pca","scale","normalize","kmeans"],
params={"pca__n_components":2,
"kmeans__n_clusters":len(set(y['target']))})
print(X2)
from streamml2.streams import ModelSelectionStream
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
import pandas as pd
from sklearn.datasets import load_boston
boston=load_boston()
X=pd.DataFrame(boston['data'], columns=boston['feature_names'])
y=pd.DataFrame(boston['target'],columns=["target"])
regression_options={"lr" : 0,
"svr" : 0,
"rfr":0,
"abr":0,
"knnr":0,
"ridge":0,
"lasso":0,
"enet":0,
"mlpr":0,
"br":0,
"dtr":0,
"gbr":0,
"gpr":0,
"hr":0,
"tsr":0,
"par":0,
"ard":0,
"bays_ridge":0,
"lasso_lar":0,
"lar":0}
results_dict = ModelSelectionStream(X,y).flow(list(regression_options.keys()),
params={},
metrics=[],
test_size=0.5,
nfolds=10,
nrepeats=10,
verbose=False,
regressors=True,
stratified=True,
cut=y['target'].mean(),
modelSelection=True,
n_jobs=3)
print("Best Models ... ")
print(results_dict["models"])
print("Final Errors ... ")
print(pd.DataFrame(results_dict["final_errors"]))
print("Metric Table ...")
print(pd.DataFrame(results_dict["avg_kfold"]))
print("Significance By Metric ...")
for k in results_dict["significance"].keys():
print(k)
print(results_dict["significance"][k])
from streamml2.streams import FeatureSelectionStream
from sklearn.datasets import load_iris
iris=load_iris()
X=pd.DataFrame(iris['data'], columns=iris['feature_names'])
y=pd.DataFrame(iris['target'], columns=['target'])
return_dict = FeatureSelectionStream(X,y).flow(["rfc", "abc", "svc"],
params={},
verbose=True,
regressors=False,
ensemble=True,
featurePercentage=0.5,
n_jobs=3)
print("Feature data ...")
print(pd.DataFrame(return_dict['feature_importances']))
print("Features rankings decision maker...")
print(return_dict['ensemble_results'])
print("Reduced data ...")
print(X[return_dict['kept_features']].head())
from sklearn.datasets import load_boston
boston=load_boston()
X=pd.DataFrame(boston['data'], columns=boston['feature_names'])
y=pd.DataFrame(boston['target'],columns=["target"])
return_dict = FeatureSelectionStream(X,y).flow(["plsr", "mixed_selection", "rfr", "abr", "svr"],
params={"mixed_selection__threshold_in":0.01,
"mixed_selection__threshold_out":0.05,
"mixed_selection__verbose":True},
verbose=True,
regressors=True,
ensemble=True,
featurePercentage=0.5,
n_jobs=3)
e.g., kmeans cluster column was appended via transformer, then dropped due to lack of significance in feature selection.
import pandas as pd
from streamml2.streams import TransformationStream
from streamml2.streams import FeatureSelectionStream
from streamml2.streams import ModelSelectionStream
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
# Load data
boston=load_boston()
X=pd.DataFrame(boston['data'], columns=boston['feature_names'])
y=pd.DataFrame(boston['target'],columns=["target"])
# Transform data
X2 = TransformationStream(X).flow(["pca","normalize","kmeans"],
params={"pca__percent_variance":0.95,
"kmeans__n_clusters":len(set('target'))})
print("Transformed Boston Dataset ... ")
print(X2)
return_dict = FeatureSelectionStream(X2,y).flow(["mixed_selection", "abr", "rfr","svr","plsr"],
params={},
regressors=True,
ensemble=True,
featurePercentage=0.50,
n_jobs=3)
print("Top 50% Critical Features ...")
print(return_dict['kept_features'])
Xsignal=X2[return_dict['kept_features']]
print(Xsignal)
regression_options={"lr" : 0,
"svr" : 0,
"rfr":0,
"abr":0,
"knnr":0,
"ridge":0,
"lasso":0,
"enet":0,
"mlpr":0,
"br":0,
"dtr":0,
"gbr":0,
"gpr":0,
"hr":0,
"tsr":0,
"par":0,
"ard":0,
"bays_ridge":0,
"lasso_lar":0,
"lar":0}
results_dict = ModelSelectionStream(Xsignal,y).flow(list(regression_options.keys()),
params={},
metrics=[],
test_size=0.25,
nfolds=10,
nrepeats=10,
regressors=True,
stratified=False,
#cut=y['target'].mean(),
modelSelection=True,
n_jobs=3)
print("Model Competition Results...")
for k in results_dict.keys():
print(k)
print(results_dict[k])
from streamml2.streams import ModelSelectionStream
from streamml2.streams import TransformationStream
from streamml2.streams import FeatureSelectionStream
from streamml2.streamml2.utils.helpers import *
X,y=get_classification_dataset()
params=get_model_selection_classifiers_params()
results_dict=ModelSelectionStream(X,y).flow(["nbc", "logr", "knnc"],
params=params,
regressors=False)
from streamml2.streams import ModelSelectionStream
from streamml2.streams import TransformationStream
from streamml2.streams import FeatureSelectionStream
from streamml2.streamml2.utils.helpers import *
X,y=get_regression_dataset()
params=get_model_selection_regressors_params()
results_dict=ModelSelectionStream(X,y).flow(["lr", "knnr", "abr"],
params=params,
regressors=True)