Quick Sklearn

## copyright, reprint indicate the source

chapter

• Loading data set SciKit-Learn
• SciKit-Learn dataset's information
• SciKit-Learn visual data using matplotlib
• SciKit-Learn Visual Data: Principal Component Analysis (PCA)
• Preprocessing data SciKit-Learn
• SciKit-Learn K-means clustering
• SciKit-Learn SVM
• Quick SciKit-Learn

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Scikit-learn is an open source Python library, which uses a unified interface to achieve a series of machine learning, pre-processing, cross-validation and visualization algorithms.

A basic example

from sklearn import neighbors, datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = datasets.load_iris()
X, y = iris.data[:, :2], iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
knn = neighbors.KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_score(y_test, y_pred)

Download Data

Data type can be NumPy array, SciPy sparse matrix, or can be converted to other array types, such as panda DataFrame like.

import numpy as np
X = np.random.random((10,5))
y = np.array(['M','M','F','F','M','F','M','M','F','F','F'])
X[X < 0.7] = 0

Data preprocessing

Standardization / Standardization

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(X_train)
standardized_X = scaler.transform(X_train)
standardized_X_test = scaler.transform(X_test)

Normalized / Normalization

from sklearn.preprocessing import Normalizer
scaler = Normalizer().fit(X_train)
normalized_X = scaler.transform(X_train)
normalized_X_test = scaler.transform(X_test)

Binarization / Binarization

from sklearn.preprocessing import Binarizer
binarizer = Binarizer(threshold=0.0).fit(X)
binary_X = binarizer.transform(X)

Category Feature Coding

from sklearn.preprocessing import LabelEncoder
enc = LabelEncoder()
y = enc.fit_transform(y)

Estimate missing values

>>>from sklearn.preprocessing import Imputer
>>>imp = Imputer(missing_values=0, strategy='mean', axis=0)
>>>imp.fit_transform(X_train)

Wherein the generator polynomial

from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(5)
oly.fit_transform(X)

Training and test data packets

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)

Create a model

Supervised learning model

Linear Regression

from sklearn.linear_model import LinearRegression
lr = LinearRegression(normalize=True)

Support vector machine (SVM)

from sklearn.svm import SVC
svc = SVC(kernel='linear')

Naive Bayes

from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()

KNN

from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()

Unsupervised Learning Model

Principal component analysis (PCA)

from sklearn.decomposition import PCA
pca = PCA(n_components=0.95)

k-means / K Means

from sklearn.cluster import KMeans
k_means = KMeans(n_clusters=3, random_state=0)

Model fitting

Supervised learning

lr.fit(X, y)
knn.fit(X_train, y_train)
svc.fit(X_train, y_train)

Unsupervised Learning

k_means.fit(X_train)
pca_model = pca.fit_transform(X_train)

Model predictions

Supervised learning

y_pred = svc.predict(np.random.random((2,5)))

y_pred = lr.predict(X_test)

y_pred = knn.predict_proba(X_test))

Unsupervised Learning

y_pred = k_means.predict(X_test)

Performance Evaluation Model

Category Index

Accuracy

knn.score(X_test, y_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_pred)

Category Report

from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred)))

Confusion matrix

from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred)))

Return Index

The average absolute error

from sklearn.metrics import mean_absolute_error
y_true = [3, -0.5, 2])
mean_absolute_error(y_true, y_pred))

Mean square error

from sklearn.metrics import mean_squared_error
mean_squared_error(y_test, y_pred))

$ R ^ 2 $ Score

from sklearn.metrics import r2_score
r2_score(y_true, y_pred))

Clustering index

Rand adjustment factor

from sklearn.metrics import adjusted_rand_score
adjusted_rand_score(y_true, y_pred))

Homogeneity / Homogeneity

from sklearn.metrics import homogeneity_score
homogeneity_score(y_true, y_pred))

Harmonic average index / V-measure

from sklearn.metrics import v_measure_score
metrics.v_measure_score(y_true, y_pred))

Cross-validation

print(cross_val_score(knn, X_train, y_train, cv=4))
print(cross_val_score(lr, X, y, cv=2))

Model Tuning

Grid search

from sklearn.grid_search import GridSearchCV

params = {"n_neighbors": np.arange(1,3), "metric": ["euclidean", "cityblock"]}

grid = GridSearchCV(estimator=knn,param_grid=params)

grid.fit(X_train, y_train)

print(grid.best_score_)

print(grid.best_estimator_.n_neighbors)

Random parameter optimization

from sklearn.grid_search import RandomizedSearchCV

params = {"n_neighbors": range(1,5), "weights": ["uniform", "distance"]}

rsearch = RandomizedSearchCV(estimator=knn,
   param_distributions=params,
   cv=4,
   n_iter=8,
   random_state=5)

rsearch.fit(X_train, y_train)

print(rsearch.best_score_)