def test_classifier(self):
train_df = datasets.load("titanic")[["Name", "Survived"]]
y = np.array(train_df.pop("Survived"))
X_train, X_test, y_train, y_test = train_test_split(train_df,
y,
test_size=0.2,
random_state=0)
X_train = DataFrameContainer(
"TrainSet",
dataset_instance=X_train,
resource_manager=self.mock_resource_manager)
X_test = DataFrameContainer(
"TestSet",
dataset_instance=X_test,
resource_manager=self.mock_resource_manager)
y_train = NdArrayContainer("TrainLabel",
dataset_instance=y_train,
resource_manager=self.mock_resource_manager)
y_test = NdArrayContainer("TestLabel",
dataset_instance=y_test,
resource_manager=self.mock_resource_manager)
X_train.set_feature_groups(["text"])
X_test.set_feature_groups(["text"])
est_cls_list = [
TsvdTransformer,
NmfTransformer,
LsiTransformer,
LdaTransformer,
RpTransformer,
]
for cls in est_cls_list:
print("=========================")
print(cls.__name__)
print("=========================")
tokenizer = SimpleTokenlizer(
**get_default_hp_of_cls(SimpleTokenlizer))
tokenizer.in_feature_groups = "text"
tokenizer.out_feature_groups = "token"
transformer = cls(**get_default_hp_of_cls(cls))
transformer.in_feature_groups = "token"
transformer.out_feature_groups = "num"
classifier = RandomForestClassifier(
**get_default_hp_of_cls(RandomForestClassifier))
pipeline = ML_Workflow([
("tokenizer", tokenizer),
("transformer", transformer),
("classifier", classifier),
],
resource_manager=self.mock_resource_manager)
start = time()
pipeline.fit(X_train, y_train, X_test, y_test)
y_pred = pipeline.predict(X_test)
score = accuracy_score(y_test.data, y_pred)
end = time()
print("score:", score)
print("time:", end - start)
self.assertGreater(score, 0.6)
print('\n' * 2)
from matplotlib.pyplot import scatter, show, hist
def gaussian_anomaly_detection(data):
rows, cols = data.shape
mu = data.mean(axis=0)
diff = data - mu
cov = dot(diff.T, diff) / rows
a = exp(-0.5 * dot(dot(diff, inv(cov)), diff.T))
b = sqrt(pow(2 * pi, cols) * det(cov))
res = (a / b).sum(axis=1)
return res
from sklearn.datasets import load_wine as load
from sklearn.decomposition import PCA
data = PCA(2).fit_transform(load().data)
res = gaussian_anomaly_detection(data)
colors = []
for x in res:
if x < res.mean() - 2 * res.std() or x > res.mean() + 2 * res.std():
colors.append('red')
else:
colors.append('green')
scatter(data[:,0], data[:,1], c=colors)
show()
hist(res, bins=100)
show()
x_test, y_test = all_data[train_test_split:], all_labels[train_test_split:]
predictions = knn(5, x_train, y_train, x_test)
correct = 0
for i in range(len(predictions)):
if predictions[i] == y_test[i]:
correct += 1
print('Accuracy: ', correct / len(predictions))
#####################################3
from sklearn.datasets import load_iris as load
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
x_train, x_test, y_train, y_test = train_test_split(load().data,
load().target,
test_size=0.2,
shuffle=True)
predictions = knn(5, x_train, y_train, x_test)
print('My accuracy:', accuracy_score(y_test, predictions))
##########################################################################################
model = KNeighborsClassifier(n_neighbors=5,
algorithm='brute',
weights='uniform')
model.fit(x_train, y_train)
predictions = model.predict(x_test)
print('Scikit accuracy:', accuracy_score(y_test, predictions))
##########################################################################################
''' This is a snippet of code showing how to train a multiclass MKL algorithm Author: Ivano Lauriola, [email protected] ''' #load data print('loading \'iris\' dataset...', end='') from sklearn.datasets import load_iris as load ds = load() X, Y = ds.data, ds.target print('done') ''' WARNING: be sure that your matrix is not sparse! EXAMPLE: from sklearn.datasets import load_svmlight_file X,Y = load_svmlight_file(...) X = X.toarray() ''' #preprocess data print('preprocessing data...', end='') from MKLpy.preprocessing import normalization, rescale_01 X = rescale_01(X) #feature scaling in [0,1] X = normalization(X) #||X_i||_2^2 = 1 #train/test split from sklearn.model_selection import train_test_split Xtr, Xte, Ytr, Yte = train_test_split(X, Y,
from sklearn.datasets import load_digits as load from sklearn.cross_validation import train_test_split from DecisionTree import * from sklearn.metrics import accuracy_score from RF import RandomForestClassifier # from sklearn.ensemble import RandomForestClassifier data = load() X = data.data y = data.target print X.shape Xtrain, Xtest, ytrain, ytest = train_test_split(X, y) dtree = RandomForestClassifier(n_estimators=10) dtree.fit(Xtrain, ytrain) y_pred = dtree.predict(Xtest) print accuracy_score(y_pred, ytest)
# Importing Modules
from sklearn import datasets
import matplotlib.pyplot as plt
# Loading dataset
iris_df = datasets.load("/home/dottie/Downloads/datasets/dataset/dataset/test.csv")
# Available methods on dataset
print(dir(iris_df))
# Features
print(iris_df.feature_names)
# Targets
print(iris_df.target)
# Target Names
print(iris_df.target_names)
label = {0: 'red', 1: 'blue', 2: 'green'}
# Dataset Slicing
x_axis = iris_df.data[:, 0] # Sepal Length
y_axis = iris_df.data[:, 2] # Sepal Width
# Plotting
plt.scatter(x_axis, y_axis, c=iris_df.target)
plt.show()
from sklearn.datasets import load_boston as load
from sklearn.svm import SVR
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from numpy import mean
from ACA import ACATransformer
from sklearn import cluster
from sklearn.base import clone
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(*load(return_X_y=True))
def score(estimator, new_X, new_y):
estimator.fit(new_X, new_y)
predictions = estimator.predict(X_test)
error = mean((predictions - y_test)**2)
return -error
pipeline = Pipeline([("estimator", SVR())])
params = {
"estimator__C": [.5, 1, 2, 5, 10],
"estimator__epsilon": [.5, 1, 1.5, 2, 2.5, 3],
"estimator__kernel": ["rbf"],
}
#compare to random forest model
print(score(RandomForestRegressor(), X_train, y_train))
from sklearn.datasets import load_digits as load from sklearn.cross_validation import train_test_split from DecisionTree import * from sklearn.metrics import accuracy_score from RF import RandomForestClassifier #from sklearn.ensemble import RandomForestClassifier data = load() X = data.data y = data.target print X.shape Xtrain, Xtest, ytrain, ytest = train_test_split(X, y) dtree = RandomForestClassifier(n_estimators=10) dtree.fit(Xtrain, ytrain) y_pred = dtree.predict(Xtest) print accuracy_score(y_pred, ytest)
from RPCA import RPCA
from sklearn.datasets import load_diabetes as load
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data = load().data
q = 0.9 # q is in [0,1],and the bigger q, the more positive noise. the small q, the more negtive noise
p = 0.9 # p is in [0,1],and the bigger p, the less noise. the small p, the more noise.
noise = np.random.random(data.shape)
#what is the sign of noise?
sign = np.sign(np.random.random(data.shape) - q)
pick = np.random.random(data.shape)
pick = np.where(pick > p, 1, 0)
noise = 20 * noise * pick * sign
#where have noise point in the matrix?
sns.heatmap(pick)
ans = RPCA(data + noise,
w=0.1,
tol=1e-6,
itermax=1000,
p=1.2,
u=1e-3,
umax=1e10)
plt.figure()
sns.heatmap(data, vmax=0.2, vmin=0)
def predict(x_test, coefficients):
intercept = ones(shape=(len(x_test), 1))
x_test = append(intercept, x_test, axis=1)
return matmul(x_test, coefficients)
def mse(y_test, predictions):
sum_ = 0
for i in range(len(y_test)):
sum_ += (y_test[i] - predictions[i])**2
return sum_ / len(y_test)
data = load().data
x_train, x_test, y_train, y_test = train_test_split(data,
load().target,
test_size=0.3)
coefficients = fit(x_train, y_train)
predictions = predict(x_test, coefficients)
print(mean_absolute_error(y_test, predictions))
reg = LinearRegression(fit_intercept=True, normalize=False)
reg.fit(x_train, y_train)
predictions = reg.predict(x_test)
print(mean_absolute_error(y_test, predictions))
poly = PolynomialFeatures(2)
x_train = poly.fit_transform(x_train)
x_test = poly.fit_transform(x_test)
from sklearn.datasets import load_boston as load from sklearn.manifold import TSNE from matplotlib import pyplot as plt from ACA import ACATransformer from sklearn.cluster import KMeans as c import numpy as np X, y = load(return_X_y=True) transformer = ACATransformer(clusterer=c(n_clusters=14), return_old=False) averaged_x, averaged_y = transformer.fit_transform(X, y) all_x = np.concatenate([X, averaged_x]) reduced = TSNE(n_components=2).fit_transform(all_x) unclustered_x = reduced[0:len(X)] clustered_x = reduced[len(X):] print(len(X), len(unclustered_x)) print(len(averaged_x), len(clustered_x)) plot_x = [point[0] for point in unclustered_x] plot_y = [point[1] for point in unclustered_x] plt.scatter(plot_x, plot_y, c=y, cmap="autumn") plot_x = [point[0] for point in clustered_x] plot_y = [point[1] for point in clustered_x] plt.scatter(plot_x, plot_y, c=averaged_y, cmap="winter") plt.show()
