The open source Python ecosystem provides a standalone, versatile and powerful scientific working environment, including: NumPy, SciPy, IPython, Matplotlib, Pandas, and many others...
Scikit-Learn builds upon NumPy and SciPy and complements this scientific environment with machine learning algorithms;
By design, Scikit-Learn is non-intrusive, easy to use and easy to combine with other libraries;
Core algorithms are implemented in low-level languages.
Algorithms
Supervised learning:
Linear models (Ridge, Lasso, Elastic Net, ...)
Support Vector Machines
Tree-based methods (Random Forests, Bagging, GBRT, ...)
Nearest neighbors
Neural networks (basics)
Gaussian Processes
Feature selection
Unsupervised learning:
Clustering (KMeans, Ward, ...)
Matrix decomposition (PCA, ICA, ...)
Density estimation
Outlier detection
Model selection and evaluation:
Cross-validation
Grid-search
Lots of metrics
... and many more! (See our Reference)
Applications
Classifying signal from background events;
Diagnosing disease from symptoms;
Recognising cats in pictures;
Identifying body parts with Kinect cameras;
Data
Input data = Numpy arrays or Scipy sparse matrices ;
Algorithms are expressed using high-level operations defined on matrices or vectors (similar to MATLAB) ;
Leverage efficient low-leverage implementations ;
Keep code short and readable.
Example:
# Generate data
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=1000, centers=20, random_state=123)
labels = ["b", "r"]
y = np.take(labels, (y < 10))
print(X)
print(y[:5])[[-6.453 -8.764] [ 0.29 0.147] [-5.184 -1.253] ... [-0.231 -1.608] [-0.603 6.873] [ 2.284 4.874]] ['r' 'r' 'b' 'r' 'b']
# X is a 2 dimensional array, with 1000 rows and 2 columns
print(X.shape)
# y is a vector of 1000 elements
print(y.shape)(1000, 2) (1000,)
# Rows and columns can be accessed with lists, slices or masks
print(X[[1, 2, 3]]) # rows 1, 2 and 3
print(X[:5]) # 5 first rows
print(X[500:510, 0]) # values from row 500 to row 510 at column 0
print(X[y == "b"][:5]) # 5 first rows for which y is "b"[[ 0.29 0.147] [-5.184 -1.253] [-4.714 3.674]] [[-6.453 -8.764] [ 0.29 0.147] [-5.184 -1.253] [-4.714 3.674] [ 4.516 -2.881]] [-4.438 -2.46 4.331 -7.921 1.57 0.565 4.996 4.758 -1.604 1.101] [[-5.184 -1.253] [ 4.516 -2.881] [ 1.708 2.624] [-0.526 8.96 ] [-1.076 9.787]]
# Plot
plt.figure()
for label in labels:
mask = (y == label)
plt.scatter(X[mask, 0], X[mask, 1], c=label)
plt.xlim(-10, 10)
plt.ylim(-10, 10)
plt.show()
Loading external data
Numpy provides some simple tools for loading data from files (CSV, binary, etc);
For structured data, Pandas provides more advanced tools (CSV, JSON, Excel, HDF5, SQL, etc);
A simple and unified API
All learning algorithms in scikit-learn share a uniform and limited API consisting of complementary interfaces:
an estimator interface for building and fitting models;
a predictor interface for making predictions;
a transformer interface for converting data.
Goal: enforce a simple and consistent API to make it trivial to swap or plug algorithms.
Estimators
class Estimator(object):
def fit(self, X, y=None):
"""Fits estimator to data."""
# set state of ``self``
return self# Import the nearest neighbor class
from sklearn.neighbors import KNeighborsClassifier # Change this to try
# something else
# Set hyper-parameters, for controlling algorithm
clf = KNeighborsClassifier(n_neighbors=5)
# Learn a model from training data
clf.fit(X, y)Output:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2, weights='uniform')
# Estimator state is stored in instance attributes
clf._treeOutput:
<sklearn.neighbors.kd_tree.KDTree at 0x558b1dee6148>
Predictors
# Make predictions
print(clf.predict(X[:5])) Output:
['r' 'r' 'r' 'b' 'b']
# Compute (approximate) class probabilities
print(clf.predict_proba(X[:5]))[[0. 1. ] [0. 1. ] [0.2 0.8] [0.6 0.4] [0.8 0.2]]
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