A Basic Example¶
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from sklearn import neighbors, datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
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)
Loading The Data¶
Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices. Other types that are convertible to numeric arrays, such as Pandas DataFrame, are also acceptable.
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from sklearn import datasets
iris = datasets.load_iris()
# print(iris)
X, y = iris.data, iris.target
Split Data Training And Test Data : sklearn.model_selection¶
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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)
Preprocessing The Data : sklearn.preprocessing¶
Standardization¶
Standardize features by removing the mean and scaling to unit variance z = (x - u) / s
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from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(X_train)
standardized_X = scaler.transform(X_train)
standardized_X_test = scaler.transform(X_test)
standardized_X[:3]
Normalization¶
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from sklearn.preprocessing import Normalizer
scaler = Normalizer().fit(X_train)
normalized_X = scaler.transform(X_train)
normalized_X_test = scaler.transform(X_test)
Binarization¶
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from sklearn.preprocessing import Binarizer
binarizer = Binarizer(threshold=0.0).fit(X)
binary_X = binarizer.transform(X)
Encoding Categorical Features¶
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from sklearn.preprocessing import LabelEncoder
enc = LabelEncoder()
y = enc.fit_transform(y)
y
Imputing Missing Values¶
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from sklearn.impute import SimpleImputer
imp = SimpleImputer(missing_values=0, strategy='mean')
imp.fit_transform(X_train)
Generating Polynomial Features¶
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from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(5)
poly.fit_transform(X)
Supervised Learning Estimators¶
Linear Regression¶
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from sklearn.linear_model import LinearRegression
lr = LinearRegression(normalize=True)
Ridge regression¶
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from sklearn import linear_model
reg = linear_model.Ridge(alpha=.5)
reg.fit([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
print(reg.coef_)
print(reg.intercept_)
Support Vector Machines (SVM)¶
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from sklearn.svm import SVC
svc = SVC(kernel='linear')
Naive Bayes¶
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from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
KNN¶
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from sklearn import neighbors
knn = neighbors.KNeighborsClassifier(n_neighbors=5)
Unsupervised Learning Estimators¶
Principal Component Analysis (PCA)¶
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from sklearn.decomposition import PCA
pca = PCA(n_components=0.95)
K Means¶
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from sklearn.cluster import KMeans
k_means = KMeans(n_clusters=3, random_state=0)
Supervised learning¶
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lr.fit(X, y)
knn.fit(X_train, y_train)
svc.fit(X_train, y_train)
Unsupervised Learning¶
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k_means.fit(X_train)
pca_model = pca.fit_transform(X_train)
Supervised Estimators¶
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y_pred = svc.predict(np.random.random((2,4)))
y_pred = lr.predict(X_test)
y_pred = knn.predict_proba(X_test)
Unsupervised Estimators¶
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y_pred = k_means.predict(X_test)
Classification Metrics¶
Accuracy Score¶
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knn.score(X_test, y_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_pred)
Classification Report¶
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from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
Confusion Matrix¶
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from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred))
Regression Metrics¶
Mean Absolute Error¶
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from sklearn.metrics import mean_absolute_error
y_true = [3, -0.5, 2]
mean_absolute_error(y_true, y_pred)
Mean Squared Error¶
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from sklearn.metrics import mean_squared_error
mean_squared_error(y_test, y_pred)
R2 Score¶
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from sklearn.metrics import r2_score
r2_score(y_true, y_pred)
Clustering Metrics¶
Adjusted Rand Index¶
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from sklearn.metrics import adjusted_rand_score
adjusted_rand_score(y_true, y_pred)
Homogeneity¶
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from sklearn.metrics import homogeneity_score
homogeneity_score(y_true, y_pred)
V-measure¶
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from sklearn.metrics import v_measure_score
metrics.v_measure_score(y_true, y_pred)
Cross-Validation¶
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print(cross_val_score(knn, X_train, y_train, cv=4)
print(cross_val_score(lr, X, y, cv=2)
Grid Search¶
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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)
Randomized Parameter Optimization¶
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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_)