From abd650a641ac71caad1454ad03bec15b58b09d44 Mon Sep 17 00:00:00 2001 From: BossMouseFire Date: Sun, 15 Oct 2023 19:33:03 +0400 Subject: [PATCH] Lab2 --- abanin_daniil_lab_2/README.md | 41 +++++++++ abanin_daniil_lab_2/RadomizedLasso.py | 76 ++++++++++++++++ .../__pycache__/RadomizedLasso.cpython-39.pyc | Bin 0 -> 2674 bytes abanin_daniil_lab_2/lab2.py | 81 ++++++++++++++++++ abanin_daniil_lab_2/result.png | Bin 0 -> 182657 bytes 5 files changed, 198 insertions(+) create mode 100644 abanin_daniil_lab_2/README.md create mode 100644 abanin_daniil_lab_2/RadomizedLasso.py create mode 100644 abanin_daniil_lab_2/__pycache__/RadomizedLasso.cpython-39.pyc create mode 100644 abanin_daniil_lab_2/lab2.py create mode 100644 abanin_daniil_lab_2/result.png diff --git a/abanin_daniil_lab_2/README.md b/abanin_daniil_lab_2/README.md new file mode 100644 index 0000000..8d7d26b --- /dev/null +++ b/abanin_daniil_lab_2/README.md @@ -0,0 +1,41 @@ +## Лабораторная работа №2 + +### Ранжирование признаков + +## ПИбд-41 Абанин Даниил + +### Как запустить лабораторную работу: + +* установить python, numpy, matplotlib, sklearn +* запустить проект (стартовая точка lab2) + +### Какие технологии использовались: + +* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn +* Среда разработки `PyCharm` + +### Что делает лабораторная работа: + +* Генерирует данные и обучает такие модели, как: LinearRegression, RandomizedLasso, Recursive Feature Elimination (RFE) +* Производиться ранжирование признаков с помощью моделей LinearRegression, RandomizedLasso, Recursive Feature Elimination (RFE) +* Отображение получившихся результатов: 4 самых важных признака по среднему значению, значения признаков для каждой модели + + +### 4 самых важных признака по среднему значению +* Параметр - x4, значение - 0.56 +* Параметр - x1, значение - 0.45 +* Параметр - x2, значение - 0.33 +* Параметр - x9, значение - 0.33 + +####Linear Regression +[('x1', 1.0), ('x4', 0.69), ('x2', 0.61), ('x11', 0.59), ('x3', 0.51), ('x13', 0.48), ('x5', 0.19), ('x12', 0.19), ('x14', 0.12), ('x8', 0.03), ('x6', 0.02), ('x10', 0.01), ('x7', 0.0), ('x9', 0.0)] + +####Recursive Feature Elimination +[('x9', 1.0), ('x7', 0.86), ('x10', 0.71), ('x6', 0.57), ('x8', 0.43), ('x14', 0.29), ('x12', 0.14), ('x1', 0.0), ('x2', 0.0), ('x3', 0.0), ('x4', 0.0), ('x5', 0.0), ('x11', 0.0), ('x13', 0.0)] + +####Randomize Lasso +[('x4', 1.0), ('x2', 0.37), ('x1', 0.36), ('x5', 0.32), ('x6', 0.02), ('x8', 0.02), ('x3', 0.01), ('x7', 0.0), ('x9', 0.0), ('x10', 0.0), ('x11', 0.0), ('x12', 0.0), ('x13', 0.0), ('x14', 0.0)] + +#### Результаты: + +![Result](result.png) \ No newline at end of file diff --git a/abanin_daniil_lab_2/RadomizedLasso.py b/abanin_daniil_lab_2/RadomizedLasso.py new file mode 100644 index 0000000..8ac9681 --- /dev/null +++ b/abanin_daniil_lab_2/RadomizedLasso.py @@ -0,0 +1,76 @@ +from sklearn.utils import check_X_y, check_random_state +from sklearn.linear_model import Lasso +from scipy.sparse import issparse +from scipy import sparse + + +def _rescale_data(x, weights): + if issparse(x): + size = weights.shape[0] + weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size)) + x_rescaled = x * weight_dia + else: + x_rescaled = x * (1 - weights) + + return x_rescaled + + +class RandomizedLasso(Lasso): + """ + Randomized version of scikit-learns Lasso class. + + Randomized LASSO is a generalization of the LASSO. The LASSO penalises + the absolute value of the coefficients with a penalty term proportional + to `alpha`, but the randomized LASSO changes the penalty to a randomly + chosen value in the range `[alpha, alpha/weakness]`. + + Parameters + ---------- + weakness : float + Weakness value for randomized LASSO. Must be in (0, 1]. + + See also + -------- + sklearn.linear_model.LogisticRegression : learns logistic regression models + using the same algorithm. + """ + def __init__(self, weakness=0.5, alpha=1.0, fit_intercept=True, + precompute=False, copy_X=True, max_iter=1000, + tol=1e-4, warm_start=False, positive=False, + random_state=None, selection='cyclic'): + self.weakness = weakness + super(RandomizedLasso, self).__init__( + alpha=alpha, fit_intercept=fit_intercept, precompute=precompute, copy_X=copy_X, + max_iter=max_iter, tol=tol, warm_start=warm_start, + positive=positive, random_state=random_state, + selection=selection) + + def fit(self, X, y): + """Fit the model according to the given training data. + + Parameters + ---------- + X : {array-like, sparse matrix}, shape = [n_samples, n_features] + The training input samples. + + y : array-like, shape = [n_samples] + The target values. + """ + if not isinstance(self.weakness, float) or not (0.0 < self.weakness <= 1.0): + raise ValueError('weakness should be a float in (0, 1], got %s' % self.weakness) + + X, y = check_X_y(X, y, accept_sparse=True) + + n_features = X.shape[1] + weakness = 1. - self.weakness + random_state = check_random_state(self.random_state) + + weights = weakness * random_state.randint(0, 1 + 1, size=(n_features,)) + + # TODO: I am afraid this will do double normalization if set to true + #X, y, _, _ = _preprocess_data(X, y, self.fit_intercept, normalize=self.normalize, copy=False, + # sample_weight=None, return_mean=False) + + # TODO: Check if this is a problem if it happens before standardization + X_rescaled = _rescale_data(X, weights) + return super(RandomizedLasso, self).fit(X_rescaled, y) diff --git a/abanin_daniil_lab_2/__pycache__/RadomizedLasso.cpython-39.pyc b/abanin_daniil_lab_2/__pycache__/RadomizedLasso.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..24962ce3f46743acb86c9f916fefa3bfde7db8a3 GIT binary patch literal 2674 zcmZuz&2Jk;6yMoh+v~NRmNt~M6d0hDut=OXAPyC!ph8fk5~?OZcz0+Kih5zc^g$-wQp1m~{s27cGKyeBGyYPV{6U(^OcHvqnpR1ckQ z{Zl8YCBdPS1l&LHx?xgZb=u)mkm|HuQ;#=!ydAAaV{@uxCCie05GlTKR(ytQ6B@*PT?F7`EEhBNJ7p!`>u8$d5K$)Ik-;GE#TL4 ze{lD`!YRC2XW!471zZgGaaQf#rQOSPD=EES74ZmdR2mzLg% zE-$hTSji$>PemlyMzpken}K6MXkuN`8IDbBDi988Rs)Hu3B(}*3CSD@-KHx~z&ysq zX9q;w4%LA_?sQ4wblrq>y=3Nr(<4ckG!E-s-}23*br^Ij4;37F%!%7JMn|Tw zRR}~#VH|JJ`*5!`&uHGGDo(dky&yP~nWENu8ViVPCw%^`n;)&Neoj+G8SV3oOD57i zrYCarCa3m5hptUoI^-D~Qe0V4c!X`JT#PiQ_n8=RGyqrQocDTZobpU7x|8ZnP+{NI zW2(6v(4owSxkOz|l(KSqkBQ+XyEjiaM%qd#vlDM#T0-MZmi4)^n@8eukX@b>V=F4& z%oWcj2Bq0lRG-s(cdUweYX25@INQ#+Qg`o7d~=IQHsD}jdB26DVdYMb(<`(ma;EJf z2I8xUoQcX_E++;Zo8FDq+({o2~{(_cNud9rZ*{BJ+MckbGXsm9}2q;YKXzC7sn~#+Rxj02nn3_!KGY^0N*?EfA*2_m z&WGb@-2?-+8>MiWT{$ zj-0{0H-KD(N8!xztAH2*sS=;Kf9}m4*Kvt8Q(Izm>2w6ZAP8DFe-8{I?iM)tz?Byg zaQPPTVRIXFCGIv+OBu8d?InP>TjUI2^LIr15E?h}lgdNyYj>aQ1Nv@3;{dcqq=s{I zfW*2AT0XCGv1DL8mMBJ|GI!LJ=xcfC7>IR1RQ4<0S*p97+Q2_7dw5(s7 zdz8g-E)$!2xy`;lgb1oqGH^L0B%i`?QOT`C5q`_0Wa9;qZu5Cs zHYr+mhkcl1+n`tJoh*WE9}2GKX%_W3(<7*xyGMdhKH}SvIcWy@Mknt)W7ilo{!8J1 z6#S1+Ci`5MumJdrx^VQTL6xU~+nb-JBp98KM1m;6N{Bf|i+S45HNB|Tpd+`yrBEMH zd1r~OsBE0rqT57bIksCSOrhYRwP!K6>1-+%woLdXLiHz7=2GGt5_3ypV%bDCHLM<} zLfg&c>$q-<#DnT=2l7o=Fl613u?deoS{95g1nhU+aDCb?6L|qeU<)pbVIH_k@F={! z)3`up+*57{B@w!3Y9ZM>{d_^7-iCYu4qJz0sYOwe$55MCkrFL|zFrn=%!Qa1tu@w~ZOa$0kH*^a4TyirA@=vz zOvAcnrQI;CDO`_qDpcuM^r@MhZajXN@{? XqY2(G@Av_JE-C!LJV~1P^Mb}d#ht-_ literal 0 HcmV?d00001 diff --git a/abanin_daniil_lab_2/lab2.py b/abanin_daniil_lab_2/lab2.py new file mode 100644 index 0000000..6685e05 --- /dev/null +++ b/abanin_daniil_lab_2/lab2.py @@ -0,0 +1,81 @@ +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") 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