Etiqueta: Data Science

Scikit-learn es una librería de código abierto para Python, que implementa un rango de algoritmos de Machine Learning, pre-procesamiento, referencias cruzadas y visualización usando una interfaz unificada.

Un Ejemplo Básico

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)

Cargar la data

Nuestra data debe ser numérica y estar almacenada como arreglos de NumPy o matrices de SciPy. Otro tipo de data que pueda convertirse en arreglos numericos tambien se aceptan, como los DataFrames de Panda.

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

NORMALIZACIÓN

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

BINARIZACIÓN

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

CODIFICAR ATRIBUTOS CATEGÓRICOS

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

IMPUTAR VALORES FALTANTES

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

GENeraR atributos polinomiales

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

entrenaR y probaR la data

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

Crear el Modelo

Estimadores Supervisados

REGRESIÓN lineal

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

Support Vector Machines (SVM)

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

Naive Bayes

from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()

KNN

from sklearn import neighbors
knn = neighbors.KNeighborsClassifier(n_neighbors=5)

Estimadores No Supervisados

ANÁLISIS de Componente principal (PCA)

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

K Means

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

Ajustar el Modelo

Aprendizaje supervisado

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

aprendizaje no supervisado

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

Predecir

 

estimadores supervisados

y_pred = svc.predict(np.random.random((2,5)))
y_pred = lr.predict(X_test)
y_pred = knn.predict_proba(X_test))

estimadores no supervisados

y_pred = k_means.predict(X_test)

Evaluar el Desempeño del Modelo

Métricas de Clasificación

puntaje de exactitud

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

reporte de CLASIFICACIÓN

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

matriz de CONFUSIÓN

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

Métricas de Regresión

error absoluto promedio

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

error medio cuadrado

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

puntaje R2

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

Metricas de Agrupacion

ÍNDICE Ajustado en Radianes

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

Homogeneidad

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

V-measure

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

VALIDACIÓN cruzada

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

Ajustar el Modelo

BÚSQUEDA de Cuadrillas

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)

OPTIMIZACIÓN DE PARÁMETROS ALEATORIZADOS

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_)

Tomado DataCamp, donde hay una version descargable muy practica para imprimir y tener a la mano!.