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pipeline python

Innocent Iguana answered on January 30, 2023 Popularity 9/10 Helpfulness 5/10

answer pipeline python

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pipeline python

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from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, train_test_split

X = df.drop("target", axis = 1).values
y = df["target"].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21)

# Define sequential stages of your model (Only the last step should contain model, others are transformers)
steps = [('scale',StandardScaler()), 
         ('knn', KNeighborsClassifier())]
# Construct the pipeline
pipeline = Pipeline(steps)

# Perform cross validation on pipeline
from sklearn.model_selection import cross_val_score
from sklearn.metrics import make_scorer, mean_squared_error
custom_scorer = make_scorer(mean_squared_error)
scores = cross_val_score(pipeline,X_train,y_train, scoring= custom_scorer,cv=10) # "neg_mean_squared_error"

# Perform gridsearch on pipeline
parameters = {"knn__n_neighbors": np.arange(1, 50)} # Use format: step-name + __ + parameter_name 
cv = GridSearchCV(pipeline, param_grid=parameters)
# Train
cv.fit(X_train, y_train)
# Predict
y_pred = cv.predict(X_test)

### You can break down the pipeline and add the results of each step in the output
# Create a feature union of transformers : allows you to concatenate the results of multiple transformer objects along the second axis
combined_features = FeatureUnion([
    ('scaler', scaler),
    ('poly_features', poly_features),
    ('pca', pca)
])

# Define the classifier
classifier = RandomForestClassifier(random_state=42)

# Create a pipeline with FeatureUnion and the classifier
pipeline = Pipeline([
    ('features', combined_features),
    ('classifier', classifier)
])

Popularity 9/10 Helpfulness 5/10 Language python

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Contributed on Jan 30 2023

Innocent Iguana

0 Answers Avg Quality 2/10

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pipeline in python

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Pipeline is a set of tasks happening in sequence, where the output
of a task becomes the input of the next one, until it outputs the 
final product at the end. The nice thing about pipelines is that they
make our data preparation faster. 
The purpose of a pipeline is to chain multiple steps together, where
each step in the pipeline typically applies a specific transformation 
to the data.
By using a pipeline, we can ensure that the same preprocessing steps are 
applied to both the training and test sets in the same way, avoiding data
leakage or inconsistencies.
Once we have defined our pipeline, we can fit it to our training data & 
use it to make predictions on new, unseen data.

Popularity 9/10 Helpfulness 3/10 Language python

Source: Grepper

Tags: pipeline pipelin

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Contributed on Apr 04 2023

Manish Bhusal

0 Answers Avg Quality 2/10

pipeline python

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