81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
|
from matplotlib import pyplot as plt
|
||
|
|
from sklearn.linear_model import LinearRegression
|
||
|
|
from RadomizedLasso import RandomizedLasso
|
||
|
|
from sklearn.feature_selection import RFE
|
||
|
|
from sklearn.preprocessing import MinMaxScaler
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
names = ["x%s" % i for i in range(1, 15)]
|
||
|
|
|
||
|
|
|
||
|
|
def start_point():
|
||
|
|
X,Y = generation_data()
|
||
|
|
# Линейная модель
|
||
|
|
lr = LinearRegression()
|
||
|
|
lr.fit(X, Y)
|
||
|
|
# Рекурсивное сокращение признаков
|
||
|
|
rfe = RFE(lr)
|
||
|
|
rfe.fit(X, Y)
|
||
|
|
# Случайное Лассо
|
||
|
|
randomized_lasso = RandomizedLasso(alpha=.01)
|
||
|
|
randomized_lasso.fit(X, Y)
|
||
|
|
|
||
|
|
ranks = {"Linear Regression": rank_to_dict(lr.coef_), "Recursive Feature Elimination": rank_to_dict(rfe.ranking_),
|
||
|
|
"Randomize Lasso": rank_to_dict(randomized_lasso.coef_)}
|
||
|
|
|
||
|
|
get_estimation(ranks)
|
||
|
|
print_sorted_data(ranks)
|
||
|
|
|
||
|
|
|
||
|
|
def generation_data():
|
||
|
|
np.random.seed(0)
|
||
|
|
size = 750
|
||
|
|
X = np.random.uniform(0, 1, (size, 14))
|
||
|
|
Y = (10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - .5) ** 2 +
|
||
|
|
10 * X[:, 3] + 5 * X[:, 4] ** 5 + np.random.normal(0, 1))
|
||
|
|
X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4))
|
||
|
|
return X, Y
|
||
|
|
|
||
|
|
|
||
|
|
def rank_to_dict(ranks):
|
||
|
|
ranks = np.abs(ranks)
|
||
|
|
minmax = MinMaxScaler()
|
||
|
|
ranks = minmax.fit_transform(np.array(ranks).reshape(14, 1)).ravel()
|
||
|
|
ranks = map(lambda x: round(x, 2), ranks)
|
||
|
|
return dict(zip(names, ranks))
|
||
|
|
|
||
|
|
|
||
|
|
def get_estimation(ranks: {}):
|
||
|
|
mean = {}
|
||
|
|
#«Бежим» по списку ranks
|
||
|
|
for key, value in ranks.items():
|
||
|
|
for item in value.items():
|
||
|
|
if(item[0] not in mean):
|
||
|
|
mean[item[0]] = 0
|
||
|
|
mean[item[0]] += item[1]
|
||
|
|
|
||
|
|
for key, value in mean.items():
|
||
|
|
res = value/len(ranks)
|
||
|
|
mean[key] = round(res, 2)
|
||
|
|
|
||
|
|
mean_sorted = sorted(mean.items(), key=lambda item: item[1], reverse=True)
|
||
|
|
print("Средние значения")
|
||
|
|
print(mean_sorted)
|
||
|
|
|
||
|
|
|
||
|
|
print("4 самых важных признака по среднему значению")
|
||
|
|
for item in mean_sorted[:4]:
|
||
|
|
print('Параметр - {0}, значение - {1}'.format(item[0], item[1]))
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def print_sorted_data(ranks: {}):
|
||
|
|
print()
|
||
|
|
for key, value in ranks.items():
|
||
|
|
ranks[key] = sorted(value.items(), key=lambda item: item[1], reverse=True)
|
||
|
|
for key, value in ranks.items():
|
||
|
|
print(key)
|
||
|
|
print(value)
|
||
|
|
|
||
|
|
|
||
|
|
start_point()
|