Untitled

Мария Перебейнова, [21.05.2023 0:01]
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_diabetes
 
def load_split():
    diabet = load_diabetes()
    X_train, X_test, y_train, y_test = train_test_split(diabet.data, diabet.target, random_state = 42, test_size=0.5)
    return X_train, y_train

Мария Перебейнова, [21.05.2023 0:01]
from sklearn.linear_model import LinearRegression
 
def uniform(X_train,y_train):
    ans = LinearRegression().fit(X_train, y_train)
    return ans.coef_
def predict_model(model,X_test):
    return model.predict(X_test)

Мария Перебейнова, [21.05.2023 0:01]
from sklearn.linear_model import LinearRegression
 
def uniform(X_train,y_train):
    ans = LinearRegression().fit(X_train, y_train)
    return ans.coef_
def predict_model(model,X_test):
    return model.predict(X_test)

Мария Перебейнова, [21.05.2023 0:02]
from sklearn import datasets
from sklearn.model_selection import train_test_split
 
def load_split():
    iris = datasets.load_iris()
 
    X_train, X_test, y_train, y_test = train_test_split(
        iris.data, iris.target, test_size=0.8, random_state=42)
 
    return X_train[:15],y_train[:15]

Мария Перебейнова, [21.05.2023 0:02]
from sklearn.linear_model import LogisticRegression
 
def train_model(X_train,y_train):
    ans  = LogisticRegression(random_state = 42).fit(X_train, y_train)
    return ans.coef_
 
 
def model_predict(X_test, model):
    return model.predict(X_test)[:15]

Мария Перебейнова, [21.05.2023 0:02]
from sklearn.linear_model import LogisticRegression
 
def train_model(X_train,y_train):
    ans  = LogisticRegression(random_state = 42).fit(X_train, y_train)
    return ans.coef_
 
 
def model_predict(X_test, model):
    return model.predict(X_test)[:15]

Мария Перебейнова, [21.05.2023 0:02]
from sklearn.metrics import accuracy_score
 
def accuracy(y_test,y_pred):
    return (y_test==y_pred).mean()

Мария Перебейнова, [21.05.2023 0:02]
from sklearn import preprocessing
from sklearn.datasets import load_iris
from sklearn.preprocessing import RobustScaler
import numpy as np
import pandas as pd
 
from sklearn.preprocessing import StandardScaler
import numpy as np
 
def uniform(X, Y):
    scaler = StandardScaler()
    scaled_data = scaler.fit_transform(X)
    mean = np.mean(scaled_data)
    variance = np.var(scaled_data)
    mean_rounded = round(mean, 3)
    variance_rounded = round(variance, 3)
    return mean_rounded, variance_rounded

Мария Перебейнова, [21.05.2023 0:02]
from sklearn import preprocessing
from sklearn.datasets import load_iris
from sklearn.preprocessing import RobustScaler
import numpy as np
import pandas as pd
 
from sklearn.preprocessing import MinMaxScaler
import numpy as np
 
def uniform(X, Y):
 
    scaler = MinMaxScaler()
    res = scaler.fit_transform(X,Y)
 
    mo = res.mean()
    ds = res.std()
    mean = np.round(mo, 3)
    variance = np.round(ds, 3)
 
    return mean, variance

Мария Перебейнова, [21.05.2023 0:02]
from sklearn import preprocessing
from sklearn.datasets import load_iris
import numpy as np
import pandas as pd

from sklearn.preprocessing import MaxAbsScaler
import numpy as np
 
def uniform(X, Y):
 
    scaler = MaxAbsScaler()
    res = scaler.fit_transform(X,Y)
 
    mo = res.mean()
    ds = res.std()
    mean = np.round(mo, 3)
    variance = np.round(ds, 3)
 
    return mean, variance

Мария Перебейнова, [21.05.2023 0:03]
from sklearn.datasets import load_iris

import numpy as np
import pandas as pd
 
from sklearn import preprocessing

from sklearn.preprocessing import RobustScaler

def uniform(X, Y):
 
    scaler = RobustScaler()
    res = scaler.fit_transform(X,Y)
 
    mo = res.mean()
    ds = res.std()
    mean = np.round(mo, 3)
    variance = np.round(ds, 3)
 
    return mean, variance

Мария Перебейнова, [21.05.2023 0:03]
from sklearn.cluster import KMeans
def predict_model(X, y):
    ans = KMeans(n_clusters = 3, random_state =42, n_init = "auto").fit(X,y)
    return ans.cluster_centers_

Мария Перебейнова, [21.05.2023 0:03]
from sklearn import datasets

def load_split():
    res = datasets.load_wine()
    return res.data, res.target

Мария Перебейнова, [21.05.2023 0:04]
from sklearn.cluster import DBSCAN
from sklearn import preprocessing
from sklearn.datasets import load_iris

def load_data():
    res = load_iris()
    return preprocessing.StandardScaler().fit_transform(res.data)

Мария Перебейнова, [21.05.2023 0:04]
from sklearn.cluster import DBSCAN
from sklearn import preprocessing
from sklearn.datasets import load_iris

def predict_model(data):
    dbscan = DBSCAN(eps=0.7, min_samples=2)
 
    dbscan.fit(data)
 
    labels = dbscan.labels_
    return set(labels)

Мария Перебейнова, [21.05.2023 0:04]
from sklearn import datasets

def load_split():
    iris = datasets.load_iris()
    X_train, X_test, y_train, y_test = train_test_split(
        iris.data, iris.target, test_size=0.5, random_state=42)
    return X_train[:15], y_train[:15]

Мария Перебейнова, [21.05.2023 0:04]
from sklearn.naive_bayes import GaussianNB

from sklearn.metrics import accuracy_score
 
def uniform(X_train, X_test, y_train, y_test):
 
    gnb = GaussianNB()
 
    gnb.fit(X_train, y_train)
 
    y_pred = gnb.predict(X_test)
 
 
    accuracy = accuracy_score(y_test, y_pred)
 
 
    return accuracy

Мария Перебейнова, [21.05.2023 0:05]
это был 4 раздел все задания попорядку

Мария Перебейнова, [21.05.2023 0:05]
сейчас ещё визуализуцию

Мария Перебейнова, [21.05.2023 0:06]
def solve():
    fig = plt.figure(figsize=(10,9))
    gs = fig.add_gridspec(3,3)
    ax1 = fig.add_subplot(gs[:2, :1])
    ax2 = fig.add_subplot(gs[:1, 1:])
    ax3 = fig.add_subplot(gs[1:2, 1:2])
    ax4 = fig.add_subplot(gs[1:2, 2:3])
    ax5 = fig.add_subplot(gs[2:, :])
    return fig

Мария Перебейнова, [21.05.2023 0:06]
def solve(ax, X, Y, color, title):
    minX, maxX = min(X), max(100, max(X))
    minY, maxY = min(Y), max(1000, max(Y))

    ax.plot(X, Y, color=color)

    ax.set_xlim([minX, maxX])

    ax.set_ylim([minY, maxY])

    ax.set_title(title)

    ax.set_xlabel('X')
    ax.set_ylabel('Y')

    ax.set_xticks(X)
    ax.set_yticks(Y)

    ax.grid()
    return ax

Мария Перебейнова, [21.05.2023 0:06]


Мария Перебейнова, [21.05.2023 0:06]
def solve(ax, x, y_list, color_list, label_list, title):

    ax.set_title(title)
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    
    
    ax.plot(x, y_list[0], color=color_list[0], marker='s', markersize=5, label=label_list[0], linewidth=10)
    ax.plot(x, y_list[1], color=color_list[1], marker='x',linestyle = "dashdot", label=label_list[1], alpha=0.5)
    ax.plot(x, y_list[2], color=color_list[2], marker='D', linestyle='dashed', label=label_list[2], markeredgewidth=2, markeredgecolor='black')
    
    ax.legend()

Мария Перебейнова, [21.05.2023 0:07]


Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, df, colors):

    df.plot.line(ax=ax,color=colors, subplots = True)

Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, x, y):
    # Начерчиваем площадную диаграмму
    ax.fill_between(x, y, color='green', alpha=0.5)
    
    # Выделяем границы площадной диаграммы линией
    ax.plot(x, y, color='black')
    
    # Устанавливаем заголовок графика
    ax.set_title('Площадная диаграмма')
    
    # Устанавливаем подпись оси x
    ax.set_xlabel('X')
    
    # Устанавливаем подпись оси y
    ax.set_ylabel('Y')
    
    # Возвращаем объект типа данных AxesSubplot
    return ax

Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, x, y1, y2):
    ax.plot(x,y1)
    ax.plot(x,y2)
    ax.fill_between(x,y1,y2, where = (x>2)&(x<5))

Мария Перебейнова, [21.05.2023 0:07]


Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, X, interval_length, colors):
    min_value = min(X)
    max_value = max(X)
    intervals = np.arange(min_value, max_value + interval_length, interval_length)
    heights = []
    for i in range(len(intervals)):
        if i == len(intervals) - 1:
            count = len([x for x in X if intervals[i] <= x <= max_value])
        else:
            count = len([x for x in X if intervals[i] <= x < intervals[i + 1]])
            heights.append(count)
    
    mid_points = []
    for i in range(len(intervals)):
        if i == len(intervals) - 1:
            mid_point = 9.0
        else:
            mid_point = (intervals[i] + intervals[i + 1]) / 2
            mid_points.append(mid_point)
    
    ax.bar(mid_points, heights, width=0.5, color=colors[:len(intervals)])
    ax.set_xticks(mid_points)

Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, x_centers, heights_left, heights_right):
    ax.bar(x_centers-0.2, heights_left, width=0.4, color='orange')
    ax.bar(x_centers+0.2, heights_right, width=0.4, color='blue')
    
    ax.set_xticks(x_centers)

Мария Перебейнова, [21.05.2023 0:08]
def solve(ax, y_centers, heights_left, heights_right):
    ax.barh(y_centers, heights_left, height=0.7,color = "orange")
    ax.barh(y_centers, heights_right, left=heights_left ,height = 0.7, color = "blue")
    ax.set_yticks(y_centers)

Мария Перебейнова, [21.05.2023 0:08]
def solve(ax, values, labels, colors):
    ax.pie(values, labels=labels, colors=colors, startangle=90, wedgeprops={'linewidth': 2, 'edgecolor': 'black'})
    ax.set_title('Pie Chart')
    for text in ax.texts:
        text.set_color('orange')