Prompt:
Problem:

I need to perform hierarchical clustering(into 2 clusters) by a distance matrix describing their similarities, which is between different professors, like:

              prof1     prof2     prof3
       prof1     0        0.8     0.9
       prof2     0.8      0       0.2
       prof3     0.9      0.2     0

       data_matrix=[[0,0.8,0.9],[0.8,0,0.2],[0.9,0.2,0]]
The expected number of clusters is 2. Can it be done using scipy.cluster.hierarchy? I tried to do that but failed. Anyone can give me some advice? prefer answer in a list like [label1, label2, ...]

A:

<code>
import numpy as np
import pandas as pd
import scipy.cluster
data_matrix = load_data()
</code>
cluster_labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

Z = scipy.cluster.hierarchy.linkage(np.array(data_matrix), 'ward')
cluster_labels = scipy.cluster.hierarchy.cut_tree(Z, n_clusters=2).reshape(-1, ).tolist()

import numpy as np
import copy
import tokenize, io
from scipy.cluster.hierarchy import linkage, cut_tree

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data_matrix = [[0, 0.8, 0.9], [0.8, 0, 0.2], [0.9, 0.2, 0]]
        elif test_case_id == 2:
            data_matrix = [[0, 0.2, 0.9], [0.2, 0, 0.8], [0.9, 0.8, 0]]
        return data_matrix

    def generate_ans(data):
        data_matrix = data
        Z = linkage(np.array(data_matrix), "ward")
        cluster_labels = (
            cut_tree(Z, n_clusters=2)
            .reshape(
                -1,
            )
            .tolist()
        )
        return cluster_labels

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_equal(result, ans)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_equal(result, 1 - np.array(ans))
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import scipy.cluster
data_matrix = test_input
[insert]
result = cluster_labels
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "hierarchy" in tokens


Prompt:
Problem:

Given a distance matrix, with similarity between various fruits :

              fruit1     fruit2     fruit3
       fruit1     0        0.6     0.8
       fruit2     0.6      0       0.111
       fruit3     0.8      0.111     0
I need to perform hierarchical clustering on this data (into 2 clusters), where the above data is in the form of 2-d matrix

       simM=[[0,0.6,0.8],[0.6,0,0.111],[0.8,0.111,0]]
The expected number of clusters is 2. Can it be done using scipy.cluster.hierarchy? prefer answer in a list like [label1, label2, ...]

A:

<code>
import numpy as np
import pandas as pd
import scipy.cluster
simM = load_data()
</code>
cluster_labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

Z = scipy.cluster.hierarchy.linkage(np.array(simM), 'ward')
cluster_labels = scipy.cluster.hierarchy.cut_tree(Z, n_clusters=2).reshape(-1, ).tolist()

import numpy as np
import copy
import tokenize, io
from scipy.cluster.hierarchy import linkage, cut_tree

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data_matrix = [[0, 0.8, 0.9], [0.8, 0, 0.2], [0.9, 0.2, 0]]
        elif test_case_id == 2:
            data_matrix = [[0, 0.2, 0.9], [0.2, 0, 0.8], [0.9, 0.8, 0]]
        return data_matrix

    def generate_ans(data):
        simM = data
        Z = linkage(np.array(simM), "ward")
        cluster_labels = (
            cut_tree(Z, n_clusters=2)
            .reshape(
                -1,
            )
            .tolist()
        )
        return cluster_labels

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_equal(result, ans)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_equal(result, 1 - np.array(ans))
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import scipy.cluster
simM = test_input
[insert]
result = cluster_labels
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "hierarchy" in tokens


Prompt:
Problem:

Is there any package in Python that does data transformation like scaling and centering to eliminate skewness of data? In R this could be done using caret package:

set.seed(1)
predictors = data.frame(x1 = rnorm(1000,
                                   mean = 5,
                                   sd = 2),
                        x2 = rexp(1000,
                                  rate=10))

require(caret)

trans = preProcess(predictors,
                   c("BoxCox", "center", "scale"))
predictorsTrans = data.frame(
      trans = predict(trans, predictors))
I know about sklearn, but I was unable to find functions to do scaling and centering.
How can I use sklearn to solve this?

A:

<code>
import numpy as np
import pandas as pd
import sklearn
data = load_data()
assert type(data) == np.ndarray
</code>
centered_scaled_data = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn import preprocessing

centered_scaled_data = preprocessing.scale(data)

import numpy as np
import copy
import tokenize, io
import sklearn
from sklearn import preprocessing

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = np.array([[1, 2], [3, 2], [4, 5]])
        elif test_case_id == 2:
            data = np.array([1, 2, 3, 2, 4, 5])
        return data

    def generate_ans(data):
        data = data
        centered_scaled_data = preprocessing.scale(data)
        return centered_scaled_data

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
data = test_input
[insert]
result = centered_scaled_data
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sklearn" in tokens


Prompt:
Problem:

Is there any package in Python that does data transformation like scaling and centering to eliminate skewness of data?
I know about sklearn, but I was unable to find functions to do scaling and centering.
How can I use sklearn to solve this?

A:

<code>
import numpy as np
import pandas as pd
import sklearn
data = load_data()
assert type(data) == np.ndarray
</code>
centered_scaled_data = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn import preprocessing

centered_scaled_data = preprocessing.scale(data)

import numpy as np
import copy
import tokenize, io
import sklearn
from sklearn import preprocessing

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = np.array([[1, 2], [3, 2], [4, 5]])
        elif test_case_id == 2:
            data = np.array([1, 2, 3, 2, 4, 5])
        return data

    def generate_ans(data):
        data = data
        centered_scaled_data = preprocessing.scale(data)
        return centered_scaled_data

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
data = test_input
[insert]
result = centered_scaled_data
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sklearn" in tokens


Prompt:
Problem:

Is there any package in Python that does data transformation like Box-Cox transformation to eliminate skewness of data? In R this could be done using caret package:

set.seed(1)
predictors = data.frame(x1 = rnorm(1000,
                                   mean = 5,
                                   sd = 2),
                        x2 = rexp(1000,
                                  rate=10))

require(caret)

trans = preProcess(predictors,
                   c("BoxCox", "center", "scale"))
predictorsTrans = data.frame(
      trans = predict(trans, predictors))
I know about sklearn, but I was unable to find functions to do Box-Cox transformation.
How can I use sklearn to solve this?

A:

<code>
import numpy as np
import pandas as pd
import sklearn
data = load_data()
assert type(data) == np.ndarray
</code>
box_cox_data = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn import preprocessing

pt = preprocessing.PowerTransformer(method="box-cox")
box_cox_data = pt.fit_transform(data)

import numpy as np
import copy
import tokenize, io
import sklearn
from sklearn.preprocessing import PowerTransformer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = np.array([[1, 2], [3, 2], [4, 5]])
        return data

    def generate_ans(data):
        def ans1(data):
            data = data
            pt = PowerTransformer(method="box-cox")
            box_cox_data = pt.fit_transform(data)
            return box_cox_data

        def ans2(data):
            pt = PowerTransformer(method="box-cox", standardize=False)
            box_cox_data = pt.fit_transform(data)
            return box_cox_data

        return ans1(copy.deepcopy(data)), ans2(copy.deepcopy(data))

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_allclose(result, ans[0])
        ret = 1
    except:
        pass
    try:
        np.testing.assert_allclose(result, ans[1])
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
data = test_input
[insert]
result = box_cox_data
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sklearn" in tokens


Prompt:
Problem:

Is there any package in Python that does data transformation like Box-Cox transformation to eliminate skewness of data?
I know about sklearn, but I was unable to find functions to do Box-Cox transformation.
How can I use sklearn to solve this?

A:

<code>
import numpy as np
import pandas as pd
import sklearn
data = load_data()
assert type(data) == np.ndarray
</code>
box_cox_data = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn import preprocessing

pt = preprocessing.PowerTransformer(method="box-cox")
box_cox_data = pt.fit_transform(data)

import numpy as np
import copy
import tokenize, io
import sklearn
from sklearn.preprocessing import PowerTransformer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = np.array([[1, 2], [3, 2], [4, 5]])
        return data

    def generate_ans(data):
        def ans1(data):
            pt = PowerTransformer(method="box-cox")
            box_cox_data = pt.fit_transform(data)
            return box_cox_data

        def ans2(data):
            pt = PowerTransformer(method="box-cox", standardize=False)
            box_cox_data = pt.fit_transform(data)
            return box_cox_data

        return ans1(copy.deepcopy(data)), ans2(copy.deepcopy(data))

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_allclose(result, ans[0])
        ret = 1
    except:
        pass
    try:
        np.testing.assert_allclose(result, ans[1])
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
data = test_input
[insert]
result = box_cox_data
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sklearn" in tokens


Prompt:
Problem:

Is there any package in Python that does data transformation like Yeo-Johnson transformation to eliminate skewness of data? In R this could be done using caret package:

set.seed(1)
predictors = data.frame(x1 = rnorm(1000,
                                   mean = 5,
                                   sd = 2),
                        x2 = rexp(1000,
                                  rate=10))

require(caret)

trans = preProcess(predictors,
                   c("BoxCox", "center", "scale"))
predictorsTrans = data.frame(
      trans = predict(trans, predictors))
I know about sklearn, but I was unable to find functions to do Yeo-Johnson transformation.
How can I use sklearn to solve this?

A:

<code>
import numpy as np
import pandas as pd
import sklearn
data = load_data()
assert type(data) == np.ndarray
</code>
yeo_johnson_data = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn import preprocessing

pt = preprocessing.PowerTransformer(method="yeo-johnson")
yeo_johnson_data = pt.fit_transform(data)

import numpy as np
import copy
import tokenize, io
import sklearn
from sklearn.preprocessing import PowerTransformer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = np.array([[1, 2], [3, 2], [4, 5]])
        return data

    def generate_ans(data):
        def ans1(data):
            pt = PowerTransformer(method="yeo-johnson")
            yeo_johnson_data = pt.fit_transform(data)
            return yeo_johnson_data

        def ans2(data):
            pt = PowerTransformer(method="yeo-johnson", standardize=False)
            yeo_johnson_data = pt.fit_transform(data)
            return yeo_johnson_data

        return ans1(copy.deepcopy(data)), ans2(copy.deepcopy(data))

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_allclose(result, ans[0])
        ret = 1
    except:
        pass
    try:
        np.testing.assert_allclose(result, ans[1])
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
data = test_input
[insert]
result = yeo_johnson_data
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sklearn" in tokens


Prompt:
Problem:

Is there any package in Python that does data transformation like Yeo-Johnson transformation to eliminate skewness of data?
I know about sklearn, but I was unable to find functions to do Yeo-Johnson transformation.
How can I use sklearn to solve this?

A:

<code>
import numpy as np
import pandas as pd
import sklearn
data = load_data()
assert type(data) == np.ndarray
</code>
yeo_johnson_data = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn import preprocessing

pt = preprocessing.PowerTransformer(method="yeo-johnson")
yeo_johnson_data = pt.fit_transform(data)

import numpy as np
import copy
import tokenize, io
import sklearn
from sklearn.preprocessing import PowerTransformer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = np.array([[1, 2], [3, 2], [4, 5]])
        return data

    def generate_ans(data):
        def ans1(data):
            pt = PowerTransformer(method="yeo-johnson")
            yeo_johnson_data = pt.fit_transform(data)
            return yeo_johnson_data

        def ans2(data):
            pt = PowerTransformer(method="yeo-johnson", standardize=False)
            yeo_johnson_data = pt.fit_transform(data)
            return yeo_johnson_data

        return ans1(copy.deepcopy(data)), ans2(copy.deepcopy(data))

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_allclose(result, ans[0])
        ret = 1
    except:
        pass
    try:
        np.testing.assert_allclose(result, ans[1])
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
data = test_input
[insert]
result = yeo_johnson_data
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sklearn" in tokens


Prompt:
Problem:

Is there any way for me to preserve punctuation marks of !, ?, " and ' from my text documents using text CountVectorizer parameters in scikit-learn?
Assume that I have 'text' of str type now, how can I reach this target?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
text = load_data()
</code>
transformed_text = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import copy
import tokenize, io
from sklearn.feature_extraction.text import CountVectorizer

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            text = '\n\
            "I\'m so tired of this," she said, "I can\'t take it anymore!"\n\
            "I know how you feel," he replied, "but you have to stay strong."\n\
            "I don\'t know if I can," she said, her voice trembling.\n\
            "You can do it," he said, "I know you can."\n\
            "But what if I can\'t?" she said, her eyes filling with tears.\n\
            "You have to try," he said, "You can\'t give up."\n\
            "I don\'t know if I can," she said, her voice shaking.\n\
            "Yes, you can," he said, "I believe in you."'
        elif test_case_id == 2:
            text = """
            A: So how was your day today?
            B: It was okay, I guess. I woke up late and had to rush to get ready for work.
            A: That sounds like a pain. I hate waking up late.
            B: Yeah, it's not my favorite thing either. But at least I had a good breakfast to start the day.
            A: That's true. Breakfast is the most important meal of the day.
            B: Absolutely. I always make sure to eat a healthy breakfast before starting my day.
            A: That's a good idea. I should start doing that too.
            B: Yeah, you should definitely try it. I think you'll find that you have more energy throughout the day.
            A: I'll definitely give it a try. Thanks for the suggestion.
            B: No problem. I'm always happy to help out where I can.
            A: So what did you do after work today?
            B: I went to the gym and then came home and made dinner.
            A: That sounds like a good day. I wish I had time to go to the gym more often.
            B: Yeah, it's definitely important to make time for exercise. I try to go at least three times a week.
            A: That's a good goal. I'm going to try to make it to the gym at least twice a week from now on.
            B: That's a great idea. I'm sure you'll see a difference in your energy levels.
            A: I hope so. I'm getting kind of tired of being tired all the time.
            B: I know how you feel. But I think making some changes in your lifestyle, like going to the gym more
            often, will definitely help.
            A: I hope you're right. I'm getting kind of sick of my current routine.
            B: I know how you feel. Sometimes it's just good to mix things up a bit.
            A: I think you're right. I'm going to try to make some changes starting next week.
            B: That's a great idea. I'm sure you'll see a difference in no time.
            """
        return text

    def generate_ans(data):
        text = data
        vent = CountVectorizer(token_pattern=r"(?u)\b\w\w+\b|!|\?|\"|\'")
        transformed_text = vent.fit_transform([text])
        return transformed_text

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result.toarray(), ans.toarray())
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
text = test_input
[insert]
result = transformed_text
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "CountVectorizer" in tokens


Prompt:
Problem:

I have a csv file without headers which I'm importing into python using pandas. The last column is the target class, while the rest of the columns are pixel values for images. How can I go ahead and split this dataset into a training set and a testing set (80/20)?

Also, once that is done how would I also split each of those sets so that I can define x (all columns except the last one), and y (the last column)?

I've imported my file using:

dataset = pd.read_csv('example.csv', header=None, sep=',')
Thanks

A:

use random_state=42
<code>
import numpy as np
import pandas as pd
dataset = load_data()
</code>
x_train, x_test, y_train, y_test = ... # put solution in these variables
BEGIN SOLUTION
<code>

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(dataset.iloc[:, :-1], dataset.iloc[:, -1], test_size=0.2,
                                                    random_state=42)

import pandas as pd
import copy
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data, target = load_iris(as_frame=True, return_X_y=True)
            dataset = pd.concat([data, target], axis=1)
        return dataset

    def generate_ans(data):
        dataset = data
        x_train, x_test, y_train, y_test = train_test_split(
            dataset.iloc[:, :-1], dataset.iloc[:, -1], test_size=0.2, random_state=42
        )
        return x_train, x_test, y_train, y_test

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result[0], ans[0])
        pd.testing.assert_frame_equal(result[1], ans[1])
        pd.testing.assert_series_equal(result[2], ans[2])
        pd.testing.assert_series_equal(result[3], ans[3])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
dataset = test_input
[insert]
result = (x_train, x_test, y_train, y_test)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a dataframe whose last column is the target and the rest of the columns are the features.
Now, how can I split this dataframe dataset into a training set(80%) and a testing set(20%)?
Also, how should I meanwhile split each of those sets, so I can define x (all columns except the last one), and y (the last column)?
Anyone would like to help me will be great appreciated.

A:

use random_state=42
<code>
import numpy as np
import pandas as pd
data = load_data()
</code>
x_train, x_test, y_train, y_test = ... # put solution in these variables
BEGIN SOLUTION
<code>

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2,
                                                    random_state=42)

import pandas as pd
import copy
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data, target = load_iris(as_frame=True, return_X_y=True)
            dataset = pd.concat([data, target], axis=1)
        elif test_case_id == 2:
            data, target = load_iris(as_frame=True, return_X_y=True)
            dataset = pd.concat([data.iloc[:, :-1], target], axis=1)
        return dataset

    def generate_ans(data):
        data = data
        x_train, x_test, y_train, y_test = train_test_split(
            data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=42
        )
        return x_train, x_test, y_train, y_test

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result[0], ans[0])
        pd.testing.assert_frame_equal(result[1], ans[1])
        pd.testing.assert_series_equal(result[2], ans[2])
        pd.testing.assert_series_equal(result[3], ans[3])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
data = test_input
[insert]
result = (x_train, x_test, y_train, y_test)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a csv file without headers which I'm importing into python using pandas. The last column is the target class, while the rest of the columns are pixel values for images. How can I go ahead and split this dataset into a training set and a testing set (3 : 2)?

Also, once that is done how would I also split each of those sets so that I can define x (all columns except the last one), and y (the last column)?

I've imported my file using:

dataset = pd.read_csv('example.csv', header=None, sep=',')
Thanks

A:

use random_state=42
<code>
import numpy as np
import pandas as pd
dataset = load_data()
</code>
x_train, x_test, y_train, y_test = ... # put solution in these variables
BEGIN SOLUTION
<code>

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(dataset.iloc[:, :-1], dataset.iloc[:, -1], test_size=0.4,
                                                    random_state=42)

import pandas as pd
import copy
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data, target = load_iris(as_frame=True, return_X_y=True)
            dataset = pd.concat([data, target], axis=1)
        elif test_case_id == 2:
            data, target = load_iris(as_frame=True, return_X_y=True)
            dataset = pd.concat([data.iloc[:, :-1], target], axis=1)
        return dataset

    def generate_ans(data):
        dataset = data
        x_train, x_test, y_train, y_test = train_test_split(
            dataset.iloc[:, :-1], dataset.iloc[:, -1], test_size=0.4, random_state=42
        )
        return x_train, x_test, y_train, y_test

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result[0], ans[0])
        pd.testing.assert_frame_equal(result[1], ans[1])
        pd.testing.assert_series_equal(result[2], ans[2])
        pd.testing.assert_series_equal(result[3], ans[3])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
dataset = test_input
[insert]
result = (x_train, x_test, y_train, y_test)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a csv file without headers which I'm importing into python using pandas. The last column is the target class, while the rest of the columns are pixel values for images. How can I go ahead and split this dataset into a training set and a testing set (80/20)?

Also, once that is done how would I also split each of those sets so that I can define x (all columns except the last one), and y (the last column)?

I've imported my file using:

dataset = pd.read_csv('example.csv', header=None, sep=',')
Thanks

A:

use random_state=42
<code>
import numpy as np
import pandas as pd
dataset = load_data()
def solve(data):
    # return the solution in this function
    # x_train, y_train, x_test, y_test = solve(data)
    ### BEGIN SOLUTION

# def solve(data):
    ### BEGIN SOLUTION
    from sklearn.model_selection import train_test_split

    x_train, x_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2,
                                                        random_state=42)
    ### END SOLUTION
    # return x_train, y_train, x_test, y_test
# x_train, y_train, x_test, y_test = solve(data)

    return x_train, y_train, x_test, y_test

import pandas as pd
import copy
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data, target = load_iris(as_frame=True, return_X_y=True)
            dataset = pd.concat([data, target], axis=1)
        elif test_case_id == 2:
            data, target = load_iris(as_frame=True, return_X_y=True)
            dataset = pd.concat([data.iloc[:, :-1], target], axis=1)
        return dataset

    def generate_ans(data):
        dataset = data
        x_train, x_test, y_train, y_test = train_test_split(
            dataset.iloc[:, :-1], dataset.iloc[:, -1], test_size=0.2, random_state=42
        )
        return x_train, x_test, y_train, y_test

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result[0], ans[0])
        pd.testing.assert_frame_equal(result[1], ans[1])
        pd.testing.assert_series_equal(result[2], ans[2])
        pd.testing.assert_series_equal(result[3], ans[3])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
dataset = test_input
def solve(data):
[insert]
x_train, y_train, x_test, y_test = solve(dataset)
result = x_train, x_test, y_train, y_test
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a csv file which looks like below

date                       mse
2018-02-11                 14.34
2018-02-12                 7.24
2018-02-13                 4.5
2018-02-14                 3.5
2018-02-16                 12.67
2018-02-21                 45.66
2018-02-22                 15.33
2018-02-24                 98.44
2018-02-26                 23.55
2018-02-27                 45.12
2018-02-28                 78.44
2018-03-01                 34.11
2018-03-05                 23.33
2018-03-06                 7.45
...                        ...
Now I want to get two clusters for the mse values so that I know what values lies to which cluster and their mean.

Now since I do not have any other set of values apart from mse (I have to provide X and Y), I would like to use just mse values to get a k means cluster.For now for the other set of values, I pass it as range which is of same size as no of mse values.This is what I did

from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

df = pd.read_csv("generate_csv/all_data_device.csv", parse_dates=["date"])
f1 = df['mse'].values
# generate another list
f2 = list(range(0, len(f1)))
X = np.array(list(zip(f1, f2)))
kmeans = KMeans(n_clusters=2, n_init=10).fit(X)
labels = kmeans.predict(X)
# Centroid values
centroids = kmeans.cluster_centers_
#print(centroids)

fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(X[:, 0], X[:, 1], c=labels)
ax.scatter(centroids[:, 0], centroids[:, 1], marker='*', c='#050505', s=1000)
plt.title('K Mean Classification')
plt.show()
How can I just use the mse values to get the k means cluster? I am aware of the function 'reshape()' but not quite sure how to use it?

A:

<code>
from sklearn.cluster import KMeans
df = load_data()
</code>
labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

kmeans = KMeans(n_clusters=2, n_init=10)
labels = kmeans.fit_predict(df[['mse']])

import numpy as np
import pandas as pd
import copy
from sklearn.cluster import KMeans

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "date": [
                        "2018-02-11",
                        "2018-02-12",
                        "2018-02-13",
                        "2018-02-14",
                        "2018-02-16",
                        "2018-02-21",
                        "2018-02-22",
                        "2018-02-24",
                        "2018-02-26",
                        "2018-02-27",
                        "2018-02-28",
                        "2018-03-01",
                        "2018-03-05",
                        "2018-03-06",
                    ],
                    "mse": [
                        14.34,
                        7.24,
                        4.5,
                        3.5,
                        12.67,
                        45.66,
                        15.33,
                        98.44,
                        23.55,
                        45.12,
                        78.44,
                        34.11,
                        23.33,
                        7.45,
                    ],
                }
            )
        return df

    def generate_ans(data):
        df = data
        kmeans = KMeans(n_clusters=2, n_init=10)
        labels = kmeans.fit_predict(df[["mse"]])
        return labels

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_equal(result, ans)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_equal(result, 1 - ans)
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
df = test_input
[insert]
result = labels
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a csv file which looks like

date                       mse
2009-06-04                 3.11
2009-06-08                 3.33
2009-06-12                 7.52
...                        ...
I want to get two clusters for the mse values in order that I can know what values belongs to which cluster and I can get their mean.

Since I don't have other information apart from mse (I have to provide X and Y), I want to use mse values to get a kmeans cluster.

For the other set of values, I pass it as range which is of same size as no of mse values.
Here is my code

from sklearn.cluster import KMeans
import numpy as np
import pandas as pd

df = pd.read_csv("file.csv", parse_dates=["date"])
f1 = df['mse'].values
f2 = list(range(0, len(f1)))
X = np.array(list(zip(f1, f2)))
kmeans = KMeans(n_clusters=2, n_init=10).fit(X)
labels = kmeans.predict(X)
centroids = kmeans.cluster_centers_
What should I do? I am aware of 'reshape', but not sure how to use it.

A:

<code>
from sklearn.cluster import KMeans
df = load_data()
</code>
labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

kmeans = KMeans(n_clusters=2, n_init=10)
labels = kmeans.fit_predict(df[['mse']])

import numpy as np
import pandas as pd
import copy
from sklearn.cluster import KMeans

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "date": [
                        "2018-02-11",
                        "2018-02-12",
                        "2018-02-13",
                        "2018-02-14",
                        "2018-02-16",
                        "2018-02-21",
                        "2018-02-22",
                        "2018-02-24",
                        "2018-02-26",
                        "2018-02-27",
                        "2018-02-28",
                        "2018-03-01",
                        "2018-03-05",
                        "2018-03-06",
                    ],
                    "mse": [
                        14.34,
                        7.24,
                        4.5,
                        3.5,
                        12.67,
                        45.66,
                        15.33,
                        98.44,
                        23.55,
                        45.12,
                        78.44,
                        34.11,
                        23.33,
                        7.45,
                    ],
                }
            )
        return df

    def generate_ans(data):
        df = data
        kmeans = KMeans(n_clusters=2, n_init=10)
        labels = kmeans.fit_predict(df[["mse"]])
        return labels

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_equal(result, ans)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_equal(result, 1 - ans)
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
df = test_input
[insert]
result = labels
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

This question and answer demonstrate that when feature selection is performed using one of scikit-learn's dedicated feature selection routines, then the names of the selected features can be retrieved as follows:

np.asarray(vectorizer.get_feature_names())[featureSelector.get_support()]
For example, in the above code, featureSelector might be an instance of sklearn.feature_selection.SelectKBest or sklearn.feature_selection.SelectPercentile, since these classes implement the get_support method which returns a boolean mask or integer indices of the selected features.

When one performs feature selection via linear models penalized with the L1 norm, it's unclear how to accomplish this. sklearn.svm.LinearSVC has no get_support method and the documentation doesn't make clear how to retrieve the feature indices after using its transform method to eliminate features from a collection of samples. Am I missing something here?
Note use penalty='l1' and keep default arguments for others unless necessary

A:

<code>
import numpy as np
import pandas as pd
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus, y = load_data()
assert type(corpus) == list
assert type(y) == list
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
</code>
selected_feature_names = ... # put solution in this variable
BEGIN SOLUTION
<code>

svc = LinearSVC(penalty='l1', dual=False)
svc.fit(X, y)
selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[np.flatnonzero(svc.coef_)]

import numpy as np
import copy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            corpus = [
                "This is the first document.",
                "This document is the second document.",
                "And this is the first piece of news",
                "Is this the first document? No, it'the fourth document",
                "This is the second news",
            ]
            y = [0, 0, 1, 0, 1]
        return corpus, y

    def generate_ans(data):
        corpus, y = data
        vectorizer = TfidfVectorizer()
        X = vectorizer.fit_transform(corpus)
        svc = LinearSVC(penalty="l1", dual=False)
        svc.fit(X, y)
        selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[
            np.flatnonzero(svc.coef_)
        ]
        return selected_feature_names

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus, y = test_input
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
[insert]
result = selected_feature_names
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

When using SelectKBest or SelectPercentile in sklearn.feature_selection, it's known that we can use following code to get selected features
np.asarray(vectorizer.get_feature_names())[featureSelector.get_support()]
However, I'm not clear how to perform feature selection when using linear models like LinearSVC, since LinearSVC doesn't have a get_support method.
I can't find any other methods either. Am I missing something here? Thanks
Note use penalty='l1' and keep default arguments for others unless necessary

A:

<code>
import numpy as np
import pandas as pd
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus, y = load_data()
assert type(corpus) == list
assert type(y) == list
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
</code>
selected_feature_names = ... # put solution in this variable
BEGIN SOLUTION
<code>

svc = LinearSVC(penalty='l1', dual=False)
svc.fit(X, y)
selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[np.flatnonzero(svc.coef_)]

import numpy as np
import copy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            corpus = [
                "This is the first document.",
                "This document is the second document.",
                "And this is the first piece of news",
                "Is this the first document? No, it'the fourth document",
                "This is the second news",
            ]
            y = [0, 0, 1, 0, 1]
        return corpus, y

    def generate_ans(data):
        corpus, y = data
        vectorizer = TfidfVectorizer()
        X = vectorizer.fit_transform(corpus)
        svc = LinearSVC(penalty="l1", dual=False)
        svc.fit(X, y)
        selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[
            np.flatnonzero(svc.coef_)
        ]
        return selected_feature_names

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus, y = test_input
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
[insert]
result = selected_feature_names
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

This question and answer demonstrate that when feature selection is performed using one of scikit-learn's dedicated feature selection routines, then the names of the selected features can be retrieved as follows:

np.asarray(vectorizer.get_feature_names())[featureSelector.get_support()]
For example, in the above code, featureSelector might be an instance of sklearn.feature_selection.SelectKBest or sklearn.feature_selection.SelectPercentile, since these classes implement the get_support method which returns a boolean mask or integer indices of the selected features.

When one performs feature selection via linear models penalized with the L1 norm, it's unclear how to accomplish this. sklearn.svm.LinearSVC has no get_support method and the documentation doesn't make clear how to retrieve the feature indices after using its transform method to eliminate features from a collection of samples. Am I missing something here?
Note use penalty='l1' and keep default arguments for others unless necessary
A:

<code>
import numpy as np
import pandas as pd
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus, y = load_data()
assert type(corpus) == list
assert type(y) == list
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
def solve(corpus, y, vectorizer, X):
    # return the solution in this function
    # selected_feature_names = solve(corpus, y, vectorizer, X)
    ### BEGIN SOLUTION

# def solve(corpus, y, vectorizer, X):
    ### BEGIN SOLUTION
    svc = LinearSVC(penalty='l1', dual=False)
    svc.fit(X, y)
    selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[np.flatnonzero(svc.coef_)]
    ### END SOLUTION
    # return selected_feature_names
# selected_feature_names = solve(corpus, y, vectorizer, X)
    return selected_feature_names

import numpy as np
import copy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            corpus = [
                "This is the first document.",
                "This document is the second document.",
                "And this is the first piece of news",
                "Is this the first document? No, it'the fourth document",
                "This is the second news",
            ]
            y = [0, 0, 1, 0, 1]
        return corpus, y

    def generate_ans(data):
        corpus, y = data
        vectorizer = TfidfVectorizer()
        X = vectorizer.fit_transform(corpus)
        svc = LinearSVC(penalty="l1", dual=False)
        svc.fit(X, y)
        selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[
            np.flatnonzero(svc.coef_)
        ]
        return selected_feature_names

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus, y = test_input
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
def solve(corpus, y, vectorizer, X):
[insert]
selected_feature_names = solve(corpus, y, vectorizer, X)
result = selected_feature_names
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am trying to vectorize some data using

sklearn.feature_extraction.text.CountVectorizer.
This is the data that I am trying to vectorize:

corpus = [
 'We are looking for Java developer',
 'Frontend developer with knowledge in SQL and Jscript',
 'And this is the third one.',
 'Is this the first document?',
]
Properties of the vectorizer are defined by the code below:

vectorizer = CountVectorizer(stop_words="english",binary=True,lowercase=False,vocabulary={'Jscript','.Net','TypeScript','SQL', 'NodeJS','Angular','Mongo','CSS','Python','PHP','Photoshop','Oracle','Linux','C++',"Java",'TeamCity','Frontend','Backend','Full stack', 'UI Design', 'Web','Integration','Database design','UX'})
After I run:

X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray())
I get desired results but keywords from vocabulary are ordered alphabetically. The output looks like this:

['.Net', 'Angular', 'Backend', 'C++', 'CSS', 'Database design',
'Frontend', 'Full stack', 'Integration', 'Java', 'Jscript', 'Linux',
'Mongo', 'NodeJS', 'Oracle', 'PHP', 'Photoshop', 'Python', 'SQL',
'TeamCity', 'TypeScript', 'UI Design', 'UX', 'Web']

[
[0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
]
As you can see, the vocabulary is not in the same order as I set it above. Is there a way to change this? Thanks

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
corpus = [
    'We are looking for Java developer',
    'Frontend developer with knowledge in SQL and Jscript',
    'And this is the third one.',
    'Is this the first document?',
]
</code>
feature_names, X = ... # put solution in these variables
BEGIN SOLUTION
<code>

vectorizer = CountVectorizer(stop_words="english", binary=True, lowercase=False,
                             vocabulary=['Jscript', '.Net', 'TypeScript', 'SQL', 'NodeJS', 'Angular', 'Mongo',
                                         'CSS',
                                         'Python', 'PHP', 'Photoshop', 'Oracle', 'Linux', 'C++', "Java", 'TeamCity',
                                         'Frontend', 'Backend', 'Full stack', 'UI Design', 'Web', 'Integration',
                                         'Database design', 'UX'])
X = vectorizer.fit_transform(corpus).toarray()
feature_names = vectorizer.get_feature_names_out()

import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            corpus = [
                "We are looking for Java developer",
                "Frontend developer with knowledge in SQL and Jscript",
                "And this is the third one.",
                "Is this the first document?",
            ]
        return corpus

    def generate_ans(data):
        corpus = data
        vectorizer = CountVectorizer(
            stop_words="english",
            binary=True,
            lowercase=False,
            vocabulary=[
                "Jscript",
                ".Net",
                "TypeScript",
                "SQL",
                "NodeJS",
                "Angular",
                "Mongo",
                "CSS",
                "Python",
                "PHP",
                "Photoshop",
                "Oracle",
                "Linux",
                "C++",
                "Java",
                "TeamCity",
                "Frontend",
                "Backend",
                "Full stack",
                "UI Design",
                "Web",
                "Integration",
                "Database design",
                "UX",
            ],
        )
        X = vectorizer.fit_transform(corpus).toarray()
        feature_names = vectorizer.get_feature_names_out()
        return feature_names, X

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result[0], ans[0])
        np.testing.assert_equal(result[1], ans[1])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
corpus = test_input
[insert]
result = (feature_names, X)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am trying to vectorize some data using

sklearn.feature_extraction.text.CountVectorizer.
This is the data that I am trying to vectorize:

corpus = [
 'We are looking for Java developer',
 'Frontend developer with knowledge in SQL and Jscript',
 'And this is the third one.',
 'Is this the first document?',
]
Properties of the vectorizer are defined by the code below:

vectorizer = CountVectorizer(stop_words="english",binary=True,lowercase=False,vocabulary={'Jscript','.Net','TypeScript','NodeJS','Angular','Mongo','CSS','Python','PHP','Photoshop','Oracle','Linux','C++',"Java",'TeamCity','Frontend','Backend','Full stack', 'UI Design', 'Web','Integration','Database design','UX'})
After I run:

X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray())
I get desired results but keywords from vocabulary are ordered alphabetically. The output looks like this:

['.Net', 'Angular', 'Backend', 'C++', 'CSS', 'Database design',
'Frontend', 'Full stack', 'Integration', 'Java', 'Jscript', 'Linux',
'Mongo', 'NodeJS', 'Oracle', 'PHP', 'Photoshop', 'Python',
'TeamCity', 'TypeScript', 'UI Design', 'UX', 'Web']

[
[0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
]
As you can see, the vocabulary is not in the same order as I set it above. Is there a way to change this? Thanks

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
corpus = [
    'We are looking for Java developer',
    'Frontend developer with knowledge in SQL and Jscript',
    'And this is the third one.',
    'Is this the first document?',
]
</code>
feature_names, X = ... # put solution in these variables
BEGIN SOLUTION
<code>

vectorizer = CountVectorizer(stop_words="english", binary=True, lowercase=False,
                             vocabulary=['Jscript', '.Net', 'TypeScript', 'NodeJS', 'Angular', 'Mongo',
                                         'CSS',
                                         'Python', 'PHP', 'Photoshop', 'Oracle', 'Linux', 'C++', "Java", 'TeamCity',
                                         'Frontend', 'Backend', 'Full stack', 'UI Design', 'Web', 'Integration',
                                         'Database design', 'UX'])
X = vectorizer.fit_transform(corpus).toarray()
feature_names = vectorizer.get_feature_names_out()

import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            corpus = [
                "We are looking for Java developer",
                "Frontend developer with knowledge in SQL and Jscript",
                "And this is the third one.",
                "Is this the first document?",
            ]
        return corpus

    def generate_ans(data):
        corpus = data
        vectorizer = CountVectorizer(
            stop_words="english",
            binary=True,
            lowercase=False,
            vocabulary=[
                "Jscript",
                ".Net",
                "TypeScript",
                "NodeJS",
                "Angular",
                "Mongo",
                "CSS",
                "Python",
                "PHP",
                "Photoshop",
                "Oracle",
                "Linux",
                "C++",
                "Java",
                "TeamCity",
                "Frontend",
                "Backend",
                "Full stack",
                "UI Design",
                "Web",
                "Integration",
                "Database design",
                "UX",
            ],
        )
        X = vectorizer.fit_transform(corpus).toarray()
        feature_names = vectorizer.get_feature_names_out()
        return feature_names, X

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result[0], ans[0])
        np.testing.assert_equal(result[1], ans[1])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
corpus = test_input
[insert]
result = (feature_names, X)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am trying to vectorize some data using

sklearn.feature_extraction.text.CountVectorizer.
This is the data that I am trying to vectorize:

corpus = [
 'We are looking for Java developer',
 'Frontend developer with knowledge in SQL and Jscript',
 'And this is the third one.',
 'Is this the first document?',
]
Properties of the vectorizer are defined by the code below:

vectorizer = CountVectorizer(stop_words="english",binary=True,lowercase=False,vocabulary={'Jscript','.Net','TypeScript','SQL', 'NodeJS','Angular','Mongo','CSS','Python','PHP','Photoshop','Oracle','Linux','C++',"Java",'TeamCity','Frontend','Backend','Full stack', 'UI Design', 'Web','Integration','Database design','UX'})
After I run:

X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray())
I get desired results but keywords from vocabulary are ordered alphabetically. The output looks like this:

['.Net', 'Angular', 'Backend', 'C++', 'CSS', 'Database design',
'Frontend', 'Full stack', 'Integration', 'Java', 'Jscript', 'Linux',
'Mongo', 'NodeJS', 'Oracle', 'PHP', 'Photoshop', 'Python', 'SQL',
'TeamCity', 'TypeScript', 'UI Design', 'UX', 'Web']

[
[0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
]
As you can see, the vocabulary is not in the same order as I set it above. Is there a way to change this?
And actually, I want my result X be like following instead, if the order of vocabulary is correct, so there should be one more step
[
[1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
[1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1]
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
]
(note this is incorrect but for result explanation)
Thanks for answering!

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
corpus = [
    'We are looking for Java developer',
    'Frontend developer with knowledge in SQL and Jscript',
    'And this is the third one.',
    'Is this the first document?',
]
</code>
feature_names, X = ... # put solution in these variables
BEGIN SOLUTION
<code>

vectorizer = CountVectorizer(stop_words="english", binary=True, lowercase=False,
                             vocabulary=['Jscript', '.Net', 'TypeScript', 'SQL', 'NodeJS', 'Angular', 'Mongo',
                                         'CSS',
                                         'Python', 'PHP', 'Photoshop', 'Oracle', 'Linux', 'C++', "Java", 'TeamCity',
                                         'Frontend', 'Backend', 'Full stack', 'UI Design', 'Web', 'Integration',
                                         'Database design', 'UX'])

X = vectorizer.fit_transform(corpus).toarray()
X = 1 - X
feature_names = vectorizer.get_feature_names_out()

import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            corpus = [
                "We are looking for Java developer",
                "Frontend developer with knowledge in SQL and Jscript",
                "And this is the third one.",
                "Is this the first document?",
            ]
        return corpus

    def generate_ans(data):
        corpus = data
        vectorizer = CountVectorizer(
            stop_words="english",
            binary=True,
            lowercase=False,
            vocabulary=[
                "Jscript",
                ".Net",
                "TypeScript",
                "SQL",
                "NodeJS",
                "Angular",
                "Mongo",
                "CSS",
                "Python",
                "PHP",
                "Photoshop",
                "Oracle",
                "Linux",
                "C++",
                "Java",
                "TeamCity",
                "Frontend",
                "Backend",
                "Full stack",
                "UI Design",
                "Web",
                "Integration",
                "Database design",
                "UX",
            ],
        )
        X = vectorizer.fit_transform(corpus).toarray()
        rows, cols = X.shape
        for i in range(rows):
            for j in range(cols):
                if X[i, j] == 0:
                    X[i, j] = 1
                else:
                    X[i, j] = 0
        feature_names = vectorizer.get_feature_names_out()
        return feature_names, X

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result[0], ans[0])
        np.testing.assert_equal(result[1], ans[1])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
corpus = test_input
[insert]
result = (feature_names, X)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am trying to vectorize some data using

sklearn.feature_extraction.text.CountVectorizer.
This is the data that I am trying to vectorize:

corpus = [
 'We are looking for Java developer',
 'Frontend developer with knowledge in SQL and Jscript',
 'And this is the third one.',
 'Is this the first document?',
]
Properties of the vectorizer are defined by the code below:

vectorizer = CountVectorizer(stop_words="english",binary=True,lowercase=False,vocabulary={'Jscript','.Net','TypeScript','NodeJS','Angular','Mongo','CSS','Python','PHP','Photoshop','Oracle','Linux','C++',"Java",'TeamCity','Frontend','Backend','Full stack', 'UI Design', 'Web','Integration','Database design','UX'})
After I run:

X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray())
I get desired results but keywords from vocabulary are ordered alphabetically. The output looks like this:

['.Net', 'Angular', 'Backend', 'C++', 'CSS', 'Database design',
'Frontend', 'Full stack', 'Integration', 'Java', 'Jscript', 'Linux',
'Mongo', 'NodeJS', 'Oracle', 'PHP', 'Photoshop', 'Python',
'TeamCity', 'TypeScript', 'UI Design', 'UX', 'Web']

[
[0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
]
As you can see, the vocabulary is not in the same order as I set it above. Is there a way to change this?
And actually, I want my result X be like following instead, if the order of vocabulary is correct, so there should be one more step
[
[1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1]
[1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1]
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
]
(note this is incorrect but for result explanation)
Thanks

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
corpus = [
    'We are looking for Java developer',
    'Frontend developer with knowledge in SQL and Jscript',
    'And this is the third one.',
    'Is this the first document?',
]
</code>
feature_names, X = ... # put solution in these variables
BEGIN SOLUTION
<code>

vectorizer = CountVectorizer(stop_words="english", binary=True, lowercase=False,
                             vocabulary=['Jscript', '.Net', 'TypeScript', 'NodeJS', 'Angular', 'Mongo',
                                         'CSS',
                                         'Python', 'PHP', 'Photoshop', 'Oracle', 'Linux', 'C++', "Java", 'TeamCity',
                                         'Frontend', 'Backend', 'Full stack', 'UI Design', 'Web', 'Integration',
                                         'Database design', 'UX'])

X = vectorizer.fit_transform(corpus).toarray()
X = 1 - X
feature_names = vectorizer.get_feature_names_out()

import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            corpus = [
                "We are looking for Java developer",
                "Frontend developer with knowledge in SQL and Jscript",
                "And this is the third one.",
                "Is this the first document?",
            ]
        return corpus

    def generate_ans(data):
        corpus = data
        vectorizer = CountVectorizer(
            stop_words="english",
            binary=True,
            lowercase=False,
            vocabulary=[
                "Jscript",
                ".Net",
                "TypeScript",
                "NodeJS",
                "Angular",
                "Mongo",
                "CSS",
                "Python",
                "PHP",
                "Photoshop",
                "Oracle",
                "Linux",
                "C++",
                "Java",
                "TeamCity",
                "Frontend",
                "Backend",
                "Full stack",
                "UI Design",
                "Web",
                "Integration",
                "Database design",
                "UX",
            ],
        )
        X = vectorizer.fit_transform(corpus).toarray()
        rows, cols = X.shape
        for i in range(rows):
            for j in range(cols):
                if X[i, j] == 0:
                    X[i, j] = 1
                else:
                    X[i, j] = 0
        feature_names = vectorizer.get_feature_names_out()
        return feature_names, X

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result[0], ans[0])
        np.testing.assert_equal(result[1], ans[1])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
corpus = test_input
[insert]
result = (feature_names, X)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I'm trying to find a way to iterate code for a linear regression over many many columns, upwards of Z3. Here is a snippet of the dataframe called df1

    Time    A1      A2      A3      B1      B2      B3
1   1.00    6.64    6.82    6.79    6.70    6.95    7.02
2   2.00    6.70    6.86    6.92    NaN     NaN     NaN
3   3.00    NaN     NaN     NaN     7.07    7.27    7.40
4   4.00    7.15    7.26    7.26    7.19    NaN     NaN
5   5.00    NaN     NaN     NaN     NaN     7.40    7.51
6   5.50    7.44    7.63    7.58    7.54    NaN     NaN
7   6.00    7.62    7.86    7.71    NaN     NaN     NaN
This code returns the slope coefficient of a linear regression for the very ONE column only and concatenates the value to a numpy series called series, here is what it looks like for extracting the slope for the first column:

from sklearn.linear_model import LinearRegression

series = np.array([]) #blank list to append result

df2 = df1[~np.isnan(df1['A1'])] #removes NaN values for each column to apply sklearn function
df3 = df2[['Time','A1']]
npMatrix = np.matrix(df3)
X, Y = npMatrix[:,0], npMatrix[:,1]
slope = LinearRegression().fit(X,Y) # either this or the next line
m = slope.coef_[0]

series= np.concatenate((SGR_trips, m), axis = 0)
As it stands now, I am using this slice of code, replacing "A1" with a new column name all the way up to "Z3" and this is extremely inefficient. I know there are many easy way to do this with some modules but I have the drawback of having all these intermediate NaN values in the timeseries so it seems like I'm limited to this method, or something like it.

I tried using a for loop such as:

for col in df1.columns:
and replacing 'A1', for example with col in the code, but this does not seem to be working.

How should I do for this? Save the answers in a 1d array/list

Thank you!

A:

<code>
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
df1 = load_data()
</code>
slopes = ... # put solution in this variable
BEGIN SOLUTION
<code>

slopes = []
for col in df1.columns:
    if col == "Time":
        continue
    mask = ~np.isnan(df1[col])
    x = np.atleast_2d(df1.Time[mask].values).T
    y = np.atleast_2d(df1[col][mask].values).T
    reg = LinearRegression().fit(x, y)
    slopes.append(reg.coef_[0])
slopes = np.array(slopes).reshape(-1)


Prompt:
Problem:

I'm trying to iterate code for a linear regression over all columns, upwards of Z3. Here is a snippet of the dataframe called df1

    Time    A1      A2      A3      B1      B2      B3
1   5.00    NaN     NaN     NaN     NaN     7.40    7.51
2   5.50    7.44    7.63    7.58    7.54    NaN     NaN
3   6.00    7.62    7.86    7.71    NaN     NaN     NaN
This code returns the slope coefficient of a linear regression for the very ONE column only and concatenates the value to a numpy series called series, here is what it looks like for extracting the slope for the first column:

series = np.array([])
df2 = df1[~np.isnan(df1['A1'])]
df3 = df2[['Time','A1']]
npMatrix = np.matrix(df3)
X, Y = npMatrix[:,0], npMatrix[:,1]
slope = LinearRegression().fit(X,Y)
m = slope.coef_[0]
series= np.concatenate((SGR_trips, m), axis = 0)

As it stands now, I am using this slice of code, replacing "A1" with a new column name all the way up to "Z3" and this is extremely inefficient.
I know there are many easy way to do this with some modules, but I have the drawback of having all these intermediate NaN values in the timeseries.
So it seems like I'm limited to this method, or something like it.
I tried using a for loop such as:
for col in df1.columns:
and replacing 'A1', for example with col in the code, but this does not seem to be working.
Anyone can give me any ideas? Save the answers in a 1d array/list

A:

<code>
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
df1 = load_data()
</code>
slopes = ... # put solution in this variable
BEGIN SOLUTION
<code>

slopes = []
for col in df1.columns:
    if col == "Time":
        continue
    mask = ~np.isnan(df1[col])
    x = np.atleast_2d(df1.Time[mask].values).T
    y = np.atleast_2d(df1[col][mask].values).T
    reg = LinearRegression().fit(x, y)
    slopes.append(reg.coef_[0])
slopes = np.array(slopes).reshape(-1)