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Olivier Grisel
April 22, 2015
Technology
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How to use scikit-learn to solve machine learning problems
AutoML Hackathon - Paris - April 2015
Olivier Grisel
April 22, 2015
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Transcript
How to use scikit-learn to solve machine learning problems AutoML
Hackathon April 2015
Outline • Machine Learning refresher • scikit-learn • Demo: interactive
predictive modeling on Census Data with IPython notebook / pandas / scikit-learn • Combining models with Pipeline and parameter search
Predictive modeling ~= machine learning • Make predictions of outcome
on new data • Extract the structure of historical data • Statistical tools to summarize the training data into a executable predictive model • Alternative to hard-coded rules written by experts
type (category) # rooms (int) surface (float m2) public trans
(boolean) Apartment 3 50 TRUE House 5 254 FALSE Duplex 4 68 TRUE Apartment 2 32 TRUE
type (category) # rooms (int) surface (float m2) public trans
(boolean) Apartment 3 50 TRUE House 5 254 FALSE Duplex 4 68 TRUE Apartment 2 32 TRUE sold (float k€) 450 430 712 234
type (category) # rooms (int) surface (float m2) public trans
(boolean) Apartment 3 50 TRUE House 5 254 FALSE Duplex 4 68 TRUE Apartment 2 32 TRUE sold (float k€) 450 430 712 234 features target samples (train)
type (category) # rooms (int) surface (float m2) public trans
(boolean) Apartment 3 50 TRUE House 5 254 FALSE Duplex 4 68 TRUE Apartment 2 32 TRUE sold (float k€) 450 430 712 234 features target samples (train) Apartment 2 33 TRUE House 4 210 TRUE samples (test) ? ?
Training text docs images sounds transactions Labels Machine Learning Algorithm
Model Predictive Modeling Data Flow Feature vectors
New text doc image sound transaction Model Expected Label Predictive
Modeling Data Flow Feature vector Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors
Inventory forecasting & trends detection Predictive modeling in the wild
Personalized radios Fraud detection Virality and readers engagement Predictive maintenance Personality matching
• Library of Machine Learning algorithms • Focus on established
methods (e.g. ESL-II) • Open Source (BSD) • Simple fit / predict / transform API • Python / NumPy / SciPy / Cython • Model Assessment, Selection & Ensembles
Train data Train labels Model Fitted model Test data Predicted
labels Test labels Evaluation model = ModelClass(**hyperparams) model.fit(X_train, y_train)
Train data Train labels Model Fitted model Test data Predicted
labels Test labels Evaluation model = ModelClass(**hyperparams) model.fit(X_train, y_train) y_pred = model.predict(X_test)
Train data Train labels Model Fitted model Test data Predicted
labels Test labels Evaluation model = ModelClass(**hyperparams) model.fit(X_train, y_train) y_pred = model.predict(X_test) accuracy_score(y_test, y_pred)
Support Vector Machine from sklearn.svm import SVC model = SVC(kernel="rbf",
C=1.0, gamma=1e-4) model.fit(X_train, y_train) y_predicted = model.predict(X_test) from sklearn.metrics import f1_score f1_score(y_test, y_predicted)
Linear Classifier from sklearn.linear_model import SGDClassifier model = SGDClassifier(alpha=1e-4, penalty="elasticnet")
model.fit(X_train, y_train) y_predicted = model.predict(X_test) from sklearn.metrics import f1_score f1_score(y_test, y_predicted)
Random Forests from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators=200) model.fit(X_train,
y_train) y_predicted = model.predict(X_test) from sklearn.metrics import f1_score f1_score(y_test, y_predicted)
None
None
Demo time! http://nbviewer.ipython.org/github/ogrisel/notebooks/blob/ master/sklearn_demos/Income%20classification.ipynb https://github.com/ogrisel/notebooks
Combining Models from sklearn.preprocessing import StandardScaler from sklearn.decomposition import RandomizedPCA
from sklearn.svm import SVC scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) pca = RandomizedPCA(n_components=10) X_train_pca = pca.fit_transform(X_train_scaled) svm = SVC(C=0.1, gamma=1e-3) svm.fit(X_train_pca, y_train)
Pipeline from sklearn.preprocessing import StandardScaler from sklearn.decomposition import RandomizedPCA from
sklearn.svm import SVC from sklearn.pipeline import make_pipeline pipeline = make_pipeline( StandardScaler(), RandomizedPCA(n_components=10), SVC(C=0.1, gamma=1e-3), ) pipeline.fit(X_train, y_train)
Scoring manually stacked models scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train)
pca = RandomizedPCA(n_components=10) X_train_pca = pca.fit_transform(X_train_scaled) svm = SVC(C=0.1, gamma=1e-3) svm.fit(X_train_pca, y_train) X_test_scaled = scaler.transform(X_test) X_test_pca = pca.transform(X_test_scaled) y_pred = svm.predict(X_test_pca) accuracy_score(y_test, y_pred)
Scoring a pipeline pipeline = make_pipeline( RandomizedPCA(n_components=10), SVC(C=0.1, gamma=1e-3), )
pipeline.fit(X_train, y_train) y_pred = pipeline.predict(X_test) accuracy_score(y_test, y_pred)
Parameter search import numpy as np from sklearn.grid_search import RandomizedSearchCV
params = { 'randomizedpca__n_components': [5, 10, 20], 'svc__C': np.logspace(-3, 3, 7), 'svc__gamma': np.logspace(-6, 0, 7), } search = RandomizedSearchCV(pipeline, params, n_iter=30, cv=5) search.fit(X_train, y_train) # search.best_params_, search.grid_scores_
Thank you! • http://scikit-learn.org • https://github.com/scikit-learn/scikit-learn @ogrisel