Prompt:
Problem:

I'd like to do some operations to my df. And there is an example below.
df

Col1   Col2         Col3
 C      33     [Apple, Orange, Banana]
 A      2.5    [Apple, Grape]
 B      42     [Banana]
after the operations, the df is converted into

df

Col1   Col2   Apple   Orange   Banana   Grape
 C      33     1        1        1       0
 A      2.5    1        0        0       1
 B      42     0        0        1       0
Generally, I want this pandas column which consisting of a list of String names broken down into as many columns as the unique names.
Maybe it's like one-hot-encode them (note that value 1 representing a given name existing in a row and then 0 is absence).
Could any one give me any suggestion of pandas or sklearn methods? thanks!

A:

<code>
import pandas as pd
import numpy as np
import sklearn
df = load_data()
</code>
df_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

df_out = df.join(
    pd.DataFrame(
        mlb.fit_transform(df.pop('Col3')),
        index=df.index,
        columns=mlb.classes_))

import pandas as pd
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "Col1": ["C", "A", "B"],
                    "Col2": ["33", "2.5", "42"],
                    "Col3": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        elif test_case_id == 2:
            df = pd.DataFrame(
                {
                    "Col1": ["c", "a", "b"],
                    "Col2": ["3", "2", "4"],
                    "Col3": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        elif test_case_id == 3:
            df = pd.DataFrame(
                {
                    "Col1": ["c", "a", "b"],
                    "Col2": ["3", "2", "4"],
                    "Col3": [
                        ["Apple", "Orange", "Banana", "Watermelon"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        return df

    def generate_ans(data):
        df = data
        mlb = MultiLabelBinarizer()
        df_out = df.join(
            pd.DataFrame(
                mlb.fit_transform(df.pop("Col3")), index=df.index, columns=mlb.classes_
            )
        )
        return df_out

    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:
        for i in range(2):
            pd.testing.assert_series_equal(
                result.iloc[:, i], ans.iloc[:, i], check_dtype=False
            )
    except:
        return 0
    try:
        for c in ans.columns:
            pd.testing.assert_series_equal(result[c], ans[c], check_dtype=False)
        return 1
    except:
        pass
    try:
        for c in ans.columns:
            ans[c] = ans[c].replace(1, 2).replace(0, 1).replace(2, 0)
            pd.testing.assert_series_equal(result[c], ans[c], check_dtype=False)
        return 1
    except:
        pass
    return 0

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

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        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 would like to break down a pandas column, which is the last column, consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence).

For example, taking dataframe df

Col1   Col2    Col3          Col4
 C      33      11       [Apple, Orange, Banana]
 A      2.5     4.5      [Apple, Grape]
 B      42      14       [Banana]
 D      666     1919810  [Suica, Orange]
I would like to convert this to:

df

Col1 Col2     Col3  Apple  Banana  Grape  Orange  Suica
C   33       11      1       1      0       1      0
A  2.5      4.5      1       0      1       0      0
B   42       14      0       1      0       0      0
D  666  1919810      0       0      0       1      1
How can I use pandas/sklearn to achieve this?

A:

<code>
import pandas as pd
import numpy as np
import sklearn
df = load_data()
</code>
df_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

df_out = df.join(
    pd.DataFrame(
        mlb.fit_transform(df.pop('Col4')),
        index=df.index,
        columns=mlb.classes_))

import pandas as pd
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "Col1": ["C", "A", "B", "D"],
                    "Col2": ["33", "2.5", "42", "666"],
                    "Col3": ["11", "4.5", "14", "1919810"],
                    "Col4": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                        ["Suica", "Orange"],
                    ],
                }
            )
        elif test_case_id == 2:
            df = pd.DataFrame(
                {
                    "Col1": ["c", "a", "b", "d"],
                    "Col2": ["33", "2.5", "42", "666"],
                    "Col3": ["11", "4.5", "14", "1919810"],
                    "Col4": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                        ["Suica", "Orange"],
                    ],
                }
            )
        elif test_case_id == 3:
            df = pd.DataFrame(
                {
                    "Col1": ["C", "A", "B", "D"],
                    "Col2": ["33", "2.5", "42", "666"],
                    "Col3": ["11", "4.5", "14", "1919810"],
                    "Col4": [
                        ["Apple", "Orange", "Banana", "Watermelon"],
                        ["Apple", "Grape"],
                        ["Banana"],
                        ["Suica", "Orange"],
                    ],
                }
            )
        return df

    def generate_ans(data):
        df = data
        mlb = MultiLabelBinarizer()
        df_out = df.join(
            pd.DataFrame(
                mlb.fit_transform(df.pop("Col4")), index=df.index, columns=mlb.classes_
            )
        )
        return df_out

    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:
        for i in range(2):
            pd.testing.assert_series_equal(
                result.iloc[:, i], ans.iloc[:, i], check_dtype=False
            )
    except:
        return 0
    try:
        for c in ans.columns:
            pd.testing.assert_series_equal(result[c], ans[c], check_dtype=False)
        return 1
    except:
        pass
    try:
        for c in ans.columns:
            ans[c] = ans[c].replace(1, 2).replace(0, 1).replace(2, 0)
            pd.testing.assert_series_equal(result[c], ans[c], check_dtype=False)
        return 1
    except:
        pass
    return 0

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

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        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 would like to break down a pandas column, which is the last column, consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence).

For example, taking dataframe df

Col1   Col2         Col3
 C      33     [Apple, Orange, Banana]
 A      2.5    [Apple, Grape]
 B      42     [Banana]
I would like to convert this to:

df

Col1   Col2   Apple   Orange   Banana   Grape
 C      33     1        1        1       0
 A      2.5    1        0        0       1
 B      42     0        0        1       0
Similarly, if the original df has four columns, then should do the operation to the 4th one.
How can I use pandas/sklearn to achieve this?

A:

<code>
import pandas as pd
import numpy as np
import sklearn
df = load_data()
</code>
df_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

df_out = df.join(
    pd.DataFrame(
        mlb.fit_transform(df.pop(df.columns[-1])),
        index=df.index,
        columns=mlb.classes_))

import pandas as pd
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "Col1": ["C", "A", "B"],
                    "Col2": ["33", "2.5", "42"],
                    "Col3": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        elif test_case_id == 2:
            df = pd.DataFrame(
                {
                    "Col1": ["c", "a", "b"],
                    "Col2": ["3", "2", "4"],
                    "Col3": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        elif test_case_id == 3:
            df = pd.DataFrame(
                {
                    "Col1": ["c", "a", "b"],
                    "Col2": ["3", "2", "4"],
                    "Col3": [
                        ["Apple", "Orange", "Banana", "Watermelon"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        elif test_case_id == 4:
            df = pd.DataFrame(
                {
                    "Col1": ["C", "A", "B", "D"],
                    "Col2": ["33", "2.5", "42", "666"],
                    "Col3": ["11", "4.5", "14", "1919810"],
                    "Col4": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                        ["Suica", "Orange"],
                    ],
                }
            )
        return df

    def generate_ans(data):
        df = data
        mlb = MultiLabelBinarizer()
        df_out = df.join(
            pd.DataFrame(
                mlb.fit_transform(df.pop(df.columns[-1])),
                index=df.index,
                columns=mlb.classes_,
            )
        )
        return df_out

    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:
        for i in range(2):
            pd.testing.assert_series_equal(
                result.iloc[:, i], ans.iloc[:, i], check_dtype=False
            )
    except:
        return 0
    try:
        for c in ans.columns:
            pd.testing.assert_series_equal(result[c], ans[c], check_dtype=False)
        return 1
    except:
        pass
    try:
        for c in ans.columns:
            ans[c] = ans[c].replace(1, 2).replace(0, 1).replace(2, 0)
            pd.testing.assert_series_equal(result[c], ans[c], check_dtype=False)
        return 1
    except:
        pass
    return 0

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

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(4):
        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 would like to break down a pandas column, which is the last column, consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 0 representing a given element existing in a row and 1 in the case of absence).

For example, taking dataframe df

Col1   Col2         Col3
 C      33     [Apple, Orange, Banana]
 A      2.5    [Apple, Grape]
 B      42     [Banana]
I would like to convert this to:

df

Col1   Col2   Apple   Orange   Banana   Grape
 C      33     0        0        0       1
 A      2.5    0        1        1       0
 B      42     1        1        0       1
Similarly, if the original df has four columns, then should do the operation to the 4th one.
Could any one give me any suggestion of pandas or sklearn methods? thanks!

A:

<code>
import pandas as pd
import numpy as np
import sklearn
df = load_data()
</code>
df_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

df_out = df.join(
    pd.DataFrame(
        mlb.fit_transform(df.pop(df.columns[-1])),
        index=df.index,
        columns=mlb.classes_))
for idx in df_out.index:
    for col in mlb.classes_:
        df_out.loc[idx, col] = 1 - df_out.loc[idx, col]

import pandas as pd
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "Col1": ["C", "A", "B"],
                    "Col2": ["33", "2.5", "42"],
                    "Col3": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        elif test_case_id == 2:
            df = pd.DataFrame(
                {
                    "Col1": ["c", "a", "b"],
                    "Col2": ["3", "2", "4"],
                    "Col3": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        elif test_case_id == 3:
            df = pd.DataFrame(
                {
                    "Col1": ["c", "a", "b"],
                    "Col2": ["3", "2", "4"],
                    "Col3": [
                        ["Apple", "Orange", "Banana", "Watermelon"],
                        ["Apple", "Grape"],
                        ["Banana"],
                    ],
                }
            )
        elif test_case_id == 4:
            df = pd.DataFrame(
                {
                    "Col1": ["C", "A", "B", "D"],
                    "Col2": ["33", "2.5", "42", "666"],
                    "Col3": ["11", "4.5", "14", "1919810"],
                    "Col4": [
                        ["Apple", "Orange", "Banana"],
                        ["Apple", "Grape"],
                        ["Banana"],
                        ["Suica", "Orange"],
                    ],
                }
            )
        return df

    def generate_ans(data):
        df = data
        mlb = MultiLabelBinarizer()
        df_out = df.join(
            pd.DataFrame(
                mlb.fit_transform(df.pop(df.columns[-1])),
                index=df.index,
                columns=mlb.classes_,
            )
        )
        for idx in df_out.index:
            for col in mlb.classes_:
                df_out.loc[idx, col] = 1 - df_out.loc[idx, col]
        return df_out

    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:
        for i in range(2):
            pd.testing.assert_series_equal(
                result.iloc[:, i], ans.iloc[:, i], check_dtype=False
            )
    except:
        return 0
    try:
        for c in ans.columns:
            pd.testing.assert_series_equal(result[c], ans[c], check_dtype=False)
        return 1
    except:
        pass
    try:
        for c in ans.columns:
            ans[c] = ans[c].replace(1, 2).replace(0, 1).replace(2, 0)
            pd.testing.assert_series_equal(result[c], ans[c], check_dtype=False)
        return 1
    except:
        pass
    return 0

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

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(4):
        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 use linear SVM from scikit learn (LinearSVC) for binary classification problem. I understand that LinearSVC can give me the predicted labels, and the decision scores but I wanted probability estimates (confidence in the label). I want to continue using LinearSVC because of speed (as compared to sklearn.svm.SVC with linear kernel) Is it reasonable to use a logistic function to convert the decision scores to probabilities?

import sklearn.svm as suppmach
# Fit model:
svmmodel=suppmach.LinearSVC(penalty='l1',C=1)
predicted_test= svmmodel.predict(x_test)
predicted_test_scores= svmmodel.decision_function(x_test)
I want to check if it makes sense to obtain Probability estimates simply as [1 / (1 + exp(-x)) ] where x is the decision score.

Alternately, are there other options wrt classifiers that I can use to do this efficiently? I think import CalibratedClassifierCV(cv=5) might solve this problem.

So how to use this function to solve it? Thanks.
use default arguments unless necessary

A:

<code>
import numpy as np
import pandas as pd
import sklearn.svm as suppmach
X, y, x_test = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
assert type(x_test) == np.ndarray
# Fit model:
svmmodel=suppmach.LinearSVC()
</code>
proba = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.calibration import CalibratedClassifierCV

calibrated_svc = CalibratedClassifierCV(svmmodel, cv=5, method='sigmoid')
calibrated_svc.fit(X, y)
proba = calibrated_svc.predict_proba(x_test)

import numpy as np
import copy
import tokenize, io
import sklearn
import sklearn.svm as suppmach
from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification, load_iris

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(
                n_samples=100, n_features=2, n_redundant=0, random_state=42
            )
            x_test = X
        elif test_case_id == 2:
            X, y = load_iris(return_X_y=True)
            x_test = X
        return X, y, x_test

    def generate_ans(data):
        def ans1(data):
            X, y, x_test = data
            svmmodel = suppmach.LinearSVC(random_state=42)
            calibrated_svc = CalibratedClassifierCV(svmmodel, cv=5, method="sigmoid")
            calibrated_svc.fit(X, y)
            proba = calibrated_svc.predict_proba(x_test)
            return proba

        def ans2(data):
            X, y, x_test = data
            svmmodel = suppmach.LinearSVC(random_state=42)
            calibrated_svc = CalibratedClassifierCV(svmmodel, cv=5, method="isotonic")
            calibrated_svc.fit(X, y)
            proba = calibrated_svc.predict_proba(x_test)
            return proba

        def ans3(data):
            X, y, x_test = data
            svmmodel = suppmach.LinearSVC(random_state=42)
            calibrated_svc = CalibratedClassifierCV(
                svmmodel, cv=5, method="sigmoid", ensemble=False
            )
            calibrated_svc.fit(X, y)
            proba = calibrated_svc.predict_proba(x_test)
            return proba

        def ans4(data):
            X, y, x_test = data
            svmmodel = suppmach.LinearSVC(random_state=42)
            calibrated_svc = CalibratedClassifierCV(
                svmmodel, cv=5, method="isotonic", ensemble=False
            )
            calibrated_svc.fit(X, y)
            proba = calibrated_svc.predict_proba(x_test)
            return proba

        return (
            ans1(copy.deepcopy(data)),
            ans2(copy.deepcopy(data)),
            ans3(copy.deepcopy(data)),
            ans4(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
    try:
        np.testing.assert_allclose(result, ans[2])
        ret = 1
    except:
        pass
    try:
        np.testing.assert_allclose(result, ans[3])
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn.svm as suppmach
X, y, x_test = test_input
svmmodel=suppmach.LinearSVC(random_state=42)
[insert]
result = proba
"""

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 "CalibratedClassifierCV" in tokens


Prompt:
Problem:

I'm trying to solve some two classes classification problem. And I just use the LinearSVC from sklearn library.
I know that this LinearSVC will output the predicted labels, and also the decision scores. But actually I want probability estimates to show the confidence in the labels. If I continue to use the same sklearn method, is it possible to use a logistic function to convert the decision scores to probabilities?

import sklearn
model=sklearn.svm.LinearSVC(penalty='l1',C=1)
predicted_test= model.predict(x_predict)
predicted_test_scores= model.decision_function(x_predict)
I want to check if it makes sense to obtain Probability estimates simply as [1 / (1 + exp(-x)) ] where x is the decision score.

And I found that CalibratedClassifierCV(cv=5) seemed to be helpful to solve this problem.
Can anyone give some advice how to use this function? Thanks.
use default arguments unless necessary

A:

<code>
import numpy as np
import pandas as pd
from sklearn import svm
X, y, x_predict = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
assert type(x_predict) == np.ndarray
model = svm.LinearSVC()
</code>
proba = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.calibration import CalibratedClassifierCV

calibrated_svc = CalibratedClassifierCV(model, cv=5, method='sigmoid')
calibrated_svc.fit(X, y)
proba = calibrated_svc.predict_proba(x_predict)

import numpy as np
import copy
import tokenize, io
import sklearn
from sklearn import svm
from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification, load_iris

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(
                n_samples=100, n_features=2, n_redundant=0, random_state=42
            )
            x_predict = X
        elif test_case_id == 2:
            X, y = load_iris(return_X_y=True)
            x_predict = X
        return X, y, x_predict

    def generate_ans(data):
        def ans1(data):
            X, y, x_test = data
            svmmodel = svm.LinearSVC(random_state=42)
            calibrated_svc = CalibratedClassifierCV(svmmodel, cv=5, method="sigmoid")
            calibrated_svc.fit(X, y)
            proba = calibrated_svc.predict_proba(x_test)
            return proba

        def ans2(data):
            X, y, x_test = data
            svmmodel = svm.LinearSVC(random_state=42)
            calibrated_svc = CalibratedClassifierCV(svmmodel, cv=5, method="isotonic")
            calibrated_svc.fit(X, y)
            proba = calibrated_svc.predict_proba(x_test)
            return proba

        def ans3(data):
            X, y, x_test = data
            svmmodel = svm.LinearSVC(random_state=42)
            calibrated_svc = CalibratedClassifierCV(
                svmmodel, cv=5, method="sigmoid", ensemble=False
            )
            calibrated_svc.fit(X, y)
            proba = calibrated_svc.predict_proba(x_test)
            return proba

        def ans4(data):
            X, y, x_test = data
            svmmodel = svm.LinearSVC(random_state=42)
            calibrated_svc = CalibratedClassifierCV(
                svmmodel, cv=5, method="isotonic", ensemble=False
            )
            calibrated_svc.fit(X, y)
            proba = calibrated_svc.predict_proba(x_test)
            return proba

        return (
            ans1(copy.deepcopy(data)),
            ans2(copy.deepcopy(data)),
            ans3(copy.deepcopy(data)),
            ans4(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
    try:
        np.testing.assert_allclose(result, ans[2])
        ret = 1
    except:
        pass
    try:
        np.testing.assert_allclose(result, ans[3])
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn import svm
X, y, x_predict = test_input
model = svm.LinearSVC(random_state=42)
[insert]
result = proba
"""

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 "CalibratedClassifierCV" in tokens


Prompt:
Problem:

I have used the

sklearn.preprocessing.OneHotEncoder
to transform some data the output is scipy.sparse.csr.csr_matrix how can I merge it back into my original dataframe along with the other columns?

I tried to use pd.concat but I get

TypeError: cannot concatenate a non-NDFrame object
Thanks

A:

<code>
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
df_origin, transform_output = load_data()
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import pandas as pd
import copy
from scipy.sparse import csr_matrix

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df_origin = pd.DataFrame([[1, 1, 4], [0, 3, 0]], columns=["A", "B", "C"])
            transform_output = csr_matrix([[1, 0, 2], [0, 3, 0]])
        elif test_case_id == 2:
            df_origin = pd.DataFrame(
                [[1, 1, 4, 5], [1, 4, 1, 9]], columns=["A", "B", "C", "D"]
            )
            transform_output = csr_matrix([[1, 9, 8, 1, 0], [1, 1, 4, 5, 1]])
        return df_origin, transform_output

    def generate_ans(data):
        df_origin, transform_output = data
        df = pd.concat([df_origin, pd.DataFrame(transform_output.toarray())], axis=1)
        return df

    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, ans, check_dtype=False, check_names=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
df_origin, transform_output = test_input
[insert]
result = df
"""

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 used a sklearn function to transform some data to scipy.sparse.csr.csr_matrix.
But now I want to get a pandas DataFrame where I merge it back into my original df along with the other columns.
I tried pd.concat, but I get an error called
TypeError: cannot concatenate a non-NDFrame object
What can I do? Thanks.

A:

<code>
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
df_origin, transform_output = load_data()
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import pandas as pd
import copy
from scipy.sparse import csr_matrix

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df_origin = pd.DataFrame([[1, 1, 4], [0, 3, 0]], columns=["A", "B", "C"])
            transform_output = csr_matrix([[1, 0, 2], [0, 3, 0]])
        elif test_case_id == 2:
            df_origin = pd.DataFrame(
                [[1, 1, 4, 5], [1, 4, 1, 9]], columns=["A", "B", "C", "D"]
            )
            transform_output = csr_matrix([[1, 9, 8, 1, 0], [1, 1, 4, 5, 1]])
        return df_origin, transform_output

    def generate_ans(data):
        df_origin, transform_output = data
        df = pd.concat([df_origin, pd.DataFrame(transform_output.toarray())], axis=1)
        return df

    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, ans, check_dtype=False, check_names=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
df_origin, transform_output = test_input
[insert]
result = df
"""

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 used the

sklearn.preprocessing.OneHotEncoder
to transform some data the output is scipy.sparse.csr.csr_matrix how can I merge it back into my original dataframe along with the other columns?

I tried to use pd.concat but I get

TypeError: cannot concatenate a non-NDFrame object
Thanks

A:

<code>
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
df_origin, transform_output = load_data()
def solve(df, transform_output):
    # return the solution in this function
    # result = solve(df, transform_output)
    ### BEGIN SOLUTION

# def solve(df, transform_output):
    ### BEGIN SOLUTION
    result = pd.concat([df, pd.DataFrame(transform_output.toarray())], axis=1)
    ### END SOLUTION
    # return result
# df = solve(df_origin, transform_output)

    return result

import pandas as pd
import copy
from scipy.sparse import csr_matrix

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df_origin = pd.DataFrame([[1, 1, 4], [0, 3, 0]], columns=["A", "B", "C"])
            transform_output = csr_matrix([[1, 0, 2], [0, 3, 0]])
        elif test_case_id == 2:
            df_origin = pd.DataFrame(
                [[1, 1, 4, 5], [1, 4, 1, 9]], columns=["A", "B", "C", "D"]
            )
            transform_output = csr_matrix([[1, 9, 8, 1, 0], [1, 1, 4, 5, 1]])
        return df_origin, transform_output

    def generate_ans(data):
        df_origin, transform_output = data
        df = pd.concat([df_origin, pd.DataFrame(transform_output.toarray())], axis=1)
        return df

    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, ans, check_dtype=False, check_names=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
df_origin, transform_output = test_input
def solve(df, transform_output):
[insert]
df = solve(df_origin, transform_output)
result = df
"""

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:

Is it possible to delete or insert a step in a sklearn.pipeline.Pipeline object?

I am trying to do a grid search with or without one step in the Pipeline object. And wondering whether I can insert or delete a step in the pipeline. I saw in the Pipeline source code, there is a self.steps object holding all the steps. We can get the steps by named_steps(). Before modifying it, I want to make sure, I do not cause unexpected effects.

Here is a example code:

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
estimators = [('reduce_dim', PCA()), ('svm', SVC())]
clf = Pipeline(estimators)
clf
Is it possible that we do something like steps = clf.named_steps(), then insert or delete in this list? Does this cause undesired effect on the clf object?

A:

Delete any step
<code>
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = [('reduce_dim', PCA()), ('poly', PolynomialFeatures()), ('svm', SVC())]
clf = Pipeline(estimators)
</code>
solve this question with example variable `clf`
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVC
from sklearn.decomposition import PCA

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            estimators = [
                ("reduce_dim", PCA()),
                ("poly", PolynomialFeatures()),
                ("svm", SVC()),
            ]
        elif test_case_id == 2:
            estimators = [
                ("reduce_poly", PolynomialFeatures()),
                ("dim_svm", PCA()),
                ("extra", PCA()),
                ("sVm_233", SVC()),
            ]
        return estimators

    def generate_ans(data):
        estimators = data
        clf = Pipeline(estimators)
        clf.steps.pop(-1)
        length = len(clf.steps)
        return length

    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
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = test_input
clf = Pipeline(estimators)
[insert]
result = len(clf.steps)
"""

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:

Is it possible to delete or insert a step in a sklearn.pipeline.Pipeline object?

I am trying to do a grid search with or without one step in the Pipeline object. And wondering whether I can insert or delete a step in the pipeline. I saw in the Pipeline source code, there is a self.steps object holding all the steps. We can get the steps by named_steps(). Before modifying it, I want to make sure, I do not cause unexpected effects.

Here is a example code:

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
clf = Pipeline([('AAA', PCA()), ('BBB', LinearSVC())])
clf
Is it possible that we do something like steps = clf.named_steps(), then insert or delete in this list? Does this cause undesired effect on the clf object?

A:

Delete any step
<code>
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = [('reduce_poly', PolynomialFeatures()), ('dim_svm', PCA()), ('sVm_233', SVC())]
clf = Pipeline(estimators)
</code>
solve this question with example variable `clf`
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVC
from sklearn.decomposition import PCA

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            estimators = [
                ("reduce_poly", PolynomialFeatures()),
                ("dim_svm", PCA()),
                ("sVm_233", SVC()),
            ]
        elif test_case_id == 2:
            estimators = [
                ("reduce_poly", PolynomialFeatures()),
                ("dim_svm", PCA()),
                ("extra", PCA()),
                ("sVm_233", SVC()),
            ]
        return estimators

    def generate_ans(data):
        estimators = data
        clf = Pipeline(estimators)
        clf.steps.pop(-1)
        length = len(clf.steps)
        return length

    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
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = test_input
clf = Pipeline(estimators)
[insert]
result = len(clf.steps)
"""

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:

Is it possible to delete or insert a certain step in a sklearn.pipeline.Pipeline object?

I am trying to do a grid search with or without one step in the Pipeline object. And wondering whether I can insert or delete a step in the pipeline. I saw in the Pipeline source code, there is a self.steps object holding all the steps. We can get the steps by named_steps(). Before modifying it, I want to make sure, I do not cause unexpected effects.

Here is a example code:

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
estimators = [('reduce_dim', PCA()), ('svm', SVC())]
clf = Pipeline(estimators)
clf
Is it possible that we do something like steps = clf.named_steps(), then insert or delete in this list? Does this cause undesired effect on the clf object?

A:

Delete the 2nd step
<code>
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = [('reduce_dIm', PCA()), ('pOly', PolynomialFeatures()), ('svdm', SVC())]
clf = Pipeline(estimators)
</code>
solve this question with example variable `clf`
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVC
from sklearn.decomposition import PCA

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            estimators = [
                ("reduce_dIm", PCA()),
                ("pOly", PolynomialFeatures()),
                ("svdm", SVC()),
            ]
        elif test_case_id == 2:
            estimators = [
                ("reduce_poly", PolynomialFeatures()),
                ("dim_svm", PCA()),
                ("extra", PCA()),
                ("sVm_233", SVC()),
            ]
        return estimators

    def generate_ans(data):
        estimators = data
        clf = Pipeline(estimators)
        clf.steps.pop(1)
        names = str(clf.named_steps)
        return 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
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = test_input
clf = Pipeline(estimators)
[insert]
result = str(clf.named_steps)
"""

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:

Is it possible to delete or insert a step in a sklearn.pipeline.Pipeline object?

I am trying to do a grid search with or without one step in the Pipeline object. And wondering whether I can insert or delete a step in the pipeline. I saw in the Pipeline source code, there is a self.steps object holding all the steps. We can get the steps by named_steps(). Before modifying it, I want to make sure, I do not cause unexpected effects.

Here is a example code:

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
estimators = [('reduce_dim', PCA()), ('svm', SVC())]
clf = Pipeline(estimators)
clf
Is it possible that we do something like steps = clf.named_steps(), then insert or delete in this list? Does this cause undesired effect on the clf object?

A:

Insert any step
<code>
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = [('reduce_dim', PCA()), ('poly', PolynomialFeatures()), ('svm', SVC())]
clf = Pipeline(estimators)
</code>
solve this question with example variable `clf`
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVC
from sklearn.decomposition import PCA

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            estimators = [
                ("reduce_dim", PCA()),
                ("poly", PolynomialFeatures()),
                ("svm", SVC()),
            ]
        elif test_case_id == 2:
            estimators = [
                ("reduce_poly", PolynomialFeatures()),
                ("dim_svm", PCA()),
                ("extra", PCA()),
                ("sVm_233", SVC()),
            ]
        return estimators

    def generate_ans(data):
        estimators = data
        clf = Pipeline(estimators)
        clf.steps.insert(0, ("reduce_dim", PCA()))
        length = len(clf.steps)
        return length

    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
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = test_input
clf = Pipeline(estimators)
[insert]
result = len(clf.steps)
"""

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:

Is it possible to delete or insert a step in a sklearn.pipeline.Pipeline object?

I am trying to do a grid search with or without one step in the Pipeline object. And wondering whether I can insert or delete a step in the pipeline. I saw in the Pipeline source code, there is a self.steps object holding all the steps. We can get the steps by named_steps(). Before modifying it, I want to make sure, I do not cause unexpected effects.

Here is a example code:

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
clf = Pipeline([('AAA', PCA()), ('BBB', LinearSVC())])
clf
Is it possible that we do something like steps = clf.named_steps(), then insert or delete in this list? Does this cause undesired effect on the clf object?

A:

Insert any step
<code>
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = [('reduce_poly', PolynomialFeatures()), ('dim_svm', PCA()), ('sVm_233', SVC())]
clf = Pipeline(estimators)
</code>
solve this question with example variable `clf`
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVC
from sklearn.decomposition import PCA

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            estimators = [
                ("reduce_poly", PolynomialFeatures()),
                ("dim_svm", PCA()),
                ("sVm_233", SVC()),
            ]
        elif test_case_id == 2:
            estimators = [
                ("reduce_poly", PolynomialFeatures()),
                ("dim_svm", PCA()),
                ("extra", PCA()),
                ("sVm_233", SVC()),
            ]
        return estimators

    def generate_ans(data):
        estimators = data
        clf = Pipeline(estimators)
        clf.steps.insert(0, ("reduce_dim", PCA()))
        length = len(clf.steps)
        return length

    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
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = test_input
clf = Pipeline(estimators)
[insert]
result = len(clf.steps)
"""

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:

Is it possible to delete or insert a certain step in a sklearn.pipeline.Pipeline object?

I am trying to do a grid search with or without one step in the Pipeline object. And wondering whether I can insert or delete a step in the pipeline. I saw in the Pipeline source code, there is a self.steps object holding all the steps. We can get the steps by named_steps(). Before modifying it, I want to make sure, I do not cause unexpected effects.

Here is a example code:

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
estimators = [('reduce_dim', PCA()), ('svm', SVC())]
clf = Pipeline(estimators)
clf
Is it possible that we do something like steps = clf.named_steps(), then insert or delete in this list? Does this cause undesired effect on the clf object?

A:

Insert ('t1919810', PCA()) right before 'svdm'
<code>
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = [('reduce_dIm', PCA()), ('pOly', PolynomialFeatures()), ('svdm', SVC())]
clf = Pipeline(estimators)
</code>
solve this question with example variable `clf`
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVC
from sklearn.decomposition import PCA

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            estimators = [
                ("reduce_dIm", PCA()),
                ("pOly", PolynomialFeatures()),
                ("svdm", SVC()),
            ]
        return estimators

    def generate_ans(data):
        estimators = data
        clf = Pipeline(estimators)
        clf.steps.insert(2, ("t1919810", PCA()))
        names = str(clf.named_steps)
        return 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
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = test_input
clf = Pipeline(estimators)
[insert]
result = str(clf.named_steps)
"""

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 do hyperparemeter search with using scikit-learn's GridSearchCV on XGBoost. During gridsearch i'd like it to early stop, since it reduce search time drastically and (expecting to) have better results on my prediction/regression task. I am using XGBoost via its Scikit-Learn API.
    model = xgb.XGBRegressor()
    GridSearchCV(model, paramGrid, verbose=verbose, cv=TimeSeriesSplit(n_splits=cv).get_n_splits([trainX, trainY]), n_jobs=n_jobs, iid=iid).fit(trainX,trainY)
I tried to give early stopping parameters with using fit_params, but then it throws this error which is basically because of lack of validation set which is required for early stopping:

/opt/anaconda/anaconda3/lib/python3.5/site-packages/xgboost/callback.py in callback(env=XGBoostCallbackEnv(model=<xgboost.core.Booster o...teration=4000, rank=0, evaluation_result_list=[]))
    187         else:
    188             assert env.cvfolds is not None
    189
    190     def callback(env):
    191         """internal function"""
--> 192         score = env.evaluation_result_list[-1][1]
        score = undefined
        env.evaluation_result_list = []
    193         if len(state) == 0:
    194             init(env)
    195         best_score = state['best_score']
    196         best_iteration = state['best_iteration']
How can i apply GridSearch on XGBoost with using early_stopping_rounds?
note that I'd like to use params below
fit_params={"early_stopping_rounds":42,
            "eval_metric" : "mae",
            "eval_set" : [[testX, testY]]}

note: model is working without gridsearch, also GridSearch works without fit_params
How can I do that? Thanks.

A:

<code>
import numpy as np
import pandas as pd
import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit
gridsearch, testX, testY, trainX, trainY = load_data()
assert type(gridsearch) == sklearn.model_selection._search.GridSearchCV
assert type(trainX) == list
assert type(trainY) == list
assert type(testX) == list
assert type(testY) == list
</code>
solve this question with example variable `gridsearch` and put score in `b`, put prediction in `c`
BEGIN SOLUTION
<code>

fit_params = {"early_stopping_rounds": 42,
              "eval_metric": "mae",
              "eval_set": [[testX, testY]]}
gridsearch.fit(trainX, trainY, **fit_params)

import numpy as np
import copy
import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            trainX = [[1], [2], [3], [4], [5]]
            trainY = [1, 2, 3, 4, 5]
            testX, testY = trainX, trainY
            paramGrid = {"subsample": [0.5, 0.8]}
            model = xgb.XGBRegressor()
            gridsearch = GridSearchCV(
                model,
                paramGrid,
                cv=TimeSeriesSplit(n_splits=2).get_n_splits([trainX, trainY]),
            )
        return gridsearch, testX, testY, trainX, trainY

    def generate_ans(data):
        gridsearch, testX, testY, trainX, trainY = data
        fit_params = {
            "early_stopping_rounds": 42,
            "eval_metric": "mae",
            "eval_set": [[testX, testY]],
        }
        gridsearch.fit(trainX, trainY, **fit_params)
        b = gridsearch.score(trainX, trainY)
        c = gridsearch.predict(trainX)
        return b, c

    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[0], ans[0])
        np.testing.assert_allclose(result[1], ans[1])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit
gridsearch, testX, testY, trainX, trainY = test_input
[insert]
b = gridsearch.score(trainX, trainY)
c = gridsearch.predict(trainX)
result = (b, c)
"""

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 the best hyper-parameters using sklearn function GridSearchCV on XGBoost.
However, I'd like it to do early stop when doing gridsearch, since this could reduce a lot of search time and might gain a better result on my tasks.
Actually, I am using XGBoost via its sklearn API.
    model = xgb.XGBRegressor()
    GridSearchCV(model, paramGrid, verbose=1, cv=TimeSeriesSplit(n_splits=3).get_n_splits([trainX, trainY]), n_jobs=n_jobs, iid=iid).fit(trainX, trainY)
I don't know how to add the early stopping parameters with fit_params. I tried, but then it throws this error which is basically because early stopping needs validation set and there is a lack of it:

So how can I apply GridSearch on XGBoost with using early_stopping_rounds?
note that I'd like to use params below
fit_params={"early_stopping_rounds":42,
            "eval_metric" : "mae",
            "eval_set" : [[testX, testY]]}

note: model is working without gridsearch, also GridSearch works without fit_params
How can I do that? Thanks.

A:

<code>
import numpy as np
import pandas as pd
import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit
gridsearch, testX, testY, trainX, trainY = load_data()
assert type(gridsearch) == sklearn.model_selection._search.GridSearchCV
assert type(trainX) == list
assert type(trainY) == list
assert type(testX) == list
assert type(testY) == list
</code>
solve this question with example variable `gridsearch` and put score in `b`, put prediction in `c`
BEGIN SOLUTION
<code>

fit_params = {"early_stopping_rounds": 42,
              "eval_metric": "mae",
              "eval_set": [[testX, testY]]}
gridsearch.fit(trainX, trainY, **fit_params)

import numpy as np
import copy
import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            trainX = [[1], [2], [3], [4], [5]]
            trainY = [1, 2, 3, 4, 5]
            testX, testY = trainX, trainY
            paramGrid = {"subsample": [0.5, 0.8]}
            model = xgb.XGBRegressor()
            gridsearch = GridSearchCV(
                model,
                paramGrid,
                cv=TimeSeriesSplit(n_splits=2).get_n_splits([trainX, trainY]),
            )
        return gridsearch, testX, testY, trainX, trainY

    def generate_ans(data):
        gridsearch, testX, testY, trainX, trainY = data
        fit_params = {
            "early_stopping_rounds": 42,
            "eval_metric": "mae",
            "eval_set": [[testX, testY]],
        }
        gridsearch.fit(trainX, trainY, **fit_params)
        b = gridsearch.score(trainX, trainY)
        c = gridsearch.predict(trainX)
        return b, c

    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[0], ans[0])
        np.testing.assert_allclose(result[1], ans[1])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit
gridsearch, testX, testY, trainX, trainY = test_input
[insert]
b = gridsearch.score(trainX, trainY)
c = gridsearch.predict(trainX)
result = (b, c)
"""

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 would like to predict the probability from Logistic Regression model with cross-validation. I know you can get the cross-validation scores, but is it possible to return the values from predict_proba instead of the scores? please save the probabilities into a list or an array.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
cv = StratifiedKFold(5).split(X, y)
logreg = LogisticRegression()
</code>
proba = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.model_selection import cross_val_predict

proba = cross_val_predict(logreg, X, y, cv=cv, method='predict_proba')

import numpy as np
import copy
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict, StratifiedKFold
import sklearn
from sklearn import datasets

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            iris = datasets.load_iris()
            X = iris.data
            y = iris.target
        return X, y

    def generate_ans(data):
        def ans1(data):
            X, y = data
            cv = StratifiedKFold(5).split(X, y)
            logreg = LogisticRegression(random_state=42)
            proba = cross_val_predict(logreg, X, y, cv=cv, method="predict_proba")
            return proba

        def ans2(data):
            X, y = data
            cv = StratifiedKFold(5).split(X, y)
            logreg = LogisticRegression(random_state=42)
            proba = []
            for train, test in cv:
                logreg.fit(X[train], y[train])
                proba.append(logreg.predict_proba(X[test]))
            return proba

        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], rtol=1e-3)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_allclose(result, ans[1], rtol=1e-3)
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
X, y = test_input
cv = StratifiedKFold(5).split(X, y)
logreg = LogisticRegression(random_state=42)
[insert]
result = proba
"""

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 want to get the probability of the Logistic Regression model, while use cross-validation.
But now I'm only able to get the scores of the model, can u help me to get the probabilities?
please save the probabilities into a list or an array. thanks.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
cv = StratifiedKFold(5).split(X, y)
logreg = LogisticRegression()
</code>
proba = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.model_selection import cross_val_predict

proba = cross_val_predict(logreg, X, y, cv=cv, method='predict_proba')

import numpy as np
import copy
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict, StratifiedKFold
import sklearn
from sklearn import datasets

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            iris = datasets.load_iris()
            X = iris.data
            y = iris.target
        return X, y

    def generate_ans(data):
        def ans1(data):
            X, y = data
            cv = StratifiedKFold(5).split(X, y)
            logreg = LogisticRegression(random_state=42)
            proba = cross_val_predict(logreg, X, y, cv=cv, method="predict_proba")
            return proba

        def ans2(data):
            X, y = data
            cv = StratifiedKFold(5).split(X, y)
            logreg = LogisticRegression(random_state=42)
            proba = []
            for train, test in cv:
                logreg.fit(X[train], y[train])
                proba.append(logreg.predict_proba(X[test]))
            return proba

        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], rtol=1e-3)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_allclose(result, ans[1], rtol=1e-3)
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
X, y = test_input
cv = StratifiedKFold(5).split(X, y)
logreg = LogisticRegression(random_state=42)
[insert]
result = proba
"""

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 some data structured as below, trying to predict t from the features.

train_df

t: time to predict
f1: feature1
f2: feature2
f3:......
Can t be scaled with StandardScaler, so I instead predict t' and then inverse the StandardScaler to get back the real time?

For example:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(train_df['t'])
train_df['t']= scaler.transform(train_df['t'])
run regression model,

check score,

!! check predicted t' with real time value(inverse StandardScaler) <- possible?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
data = load_data()
scaler = StandardScaler()
scaler.fit(data)
scaled = scaler.transform(data)
</code>
inversed = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.preprocessing import StandardScaler

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = [[1, 1], [2, 3], [3, 2], [1, 1]]
        return data

    def generate_ans(data):
        data = data
        scaler = StandardScaler()
        scaler.fit(data)
        scaled = scaler.transform(data)
        inversed = scaler.inverse_transform(scaled)
        return inversed

    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
from sklearn.preprocessing import StandardScaler
data = test_input
scaler = StandardScaler()
scaler.fit(data)
scaled = scaler.transform(data)
[insert]
result = inversed
"""

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 some data structured as below, trying to predict t from the features.

train_df

t: time to predict
f1: feature1
f2: feature2
f3:......
Can t be scaled with StandardScaler, so I instead predict t' and then inverse the StandardScaler to get back the real time?

For example:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(train_df['t'])
train_df['t']= scaler.transform(train_df['t'])
run regression model,

check score,

!! check predicted t' with real time value(inverse StandardScaler) <- possible?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
data = load_data()
scaler = StandardScaler()
scaler.fit(data)
scaled = scaler.transform(data)
def solve(data, scaler, scaled):
    # return the solution in this function
    # inversed = solve(data, scaler, scaled)
    ### BEGIN SOLUTION

# def solve(data, scaler, scaled):
    ### BEGIN SOLUTION
    inversed = scaler.inverse_transform(scaled)
    ### END SOLUTION
    # return inversed
# inversed = solve(data, scaler, scaled)

    return inversed

import numpy as np
import copy
from sklearn.preprocessing import StandardScaler

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = [[1, 1], [2, 3], [3, 2], [1, 1]]
        return data

    def generate_ans(data):
        data = data
        scaler = StandardScaler()
        scaler.fit(data)
        scaled = scaler.transform(data)
        inversed = scaler.inverse_transform(scaled)
        return inversed

    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
from sklearn.preprocessing import StandardScaler
data = test_input
scaler = StandardScaler()
scaler.fit(data)
scaled = scaler.transform(data)
def solve(data, scaler, scaled):
[insert]
inversed = solve(data, scaler, scaled)
result = inversed
"""

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 silly question.

I have done Cross-validation in scikit learn and would like to make a more visual information with the values I got for each model.

However, I can not access only the template name to insert into the dataframe. Always comes with the parameters together. Is there some method of objects created to access only the name of the model, without its parameters. Or will I have to create an external list with the names for it?

I use:

for model in models:
   scores = cross_val_score(model, X, y, cv=5)
   print(f'Name model: {model} , Mean score: {scores.mean()}')
But I obtain the name with the parameters:

Name model: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False), Mean score: 0.8066782865537986
In fact I want to get the information this way:

Name Model: LinearRegression, Mean Score: 0.8066782865537986
Thanks!

A:

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

import numpy as np
import copy
from sklearn.linear_model import LinearRegression
import sklearn
from sklearn.svm import LinearSVC

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            model = LinearRegression()
        elif test_case_id == 2:
            model = LinearSVC()
        return model

    def generate_ans(data):
        model = data
        model_name = type(model).__name__
        return model_name

    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
from sklearn.linear_model import LinearRegression
model = test_input
[insert]
result = model_name
"""

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 used sklearn for Cross-validation and want to do a more visual information with the values of each model.

The problem is, I can't only get the name of the templates.
Instead, the parameters always come altogether. How can I only retrieve the name of the models without its parameters?
Or does it mean that I have to create an external list for the names?

here I have a piece of code:

for model in models:
   scores = cross_val_score(model, X, y, cv=5)
   print(f'Name model: {model} , Mean score: {scores.mean()}')
But I also obtain the parameters:

Name model: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False), Mean score: 0.8066782865537986
In fact I want to get the information this way:

Name Model: LinearRegression, Mean Score: 0.8066782865537986
Any ideas to do that? Thanks!

A:

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

import numpy as np
import copy
from sklearn.linear_model import LinearRegression
import sklearn
from sklearn.svm import LinearSVC

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            model = LinearRegression()
        elif test_case_id == 2:
            model = LinearSVC()
        return model

    def generate_ans(data):
        model = data
        model_name = type(model).__name__
        return model_name

    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
from sklearn.linear_model import LinearRegression
model = test_input
[insert]
result = model_name
"""

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 silly question.

I have done Cross-validation in scikit learn and would like to make a more visual information with the values I got for each model.

However, I can not access only the template name to insert into the dataframe. Always comes with the parameters together. Is there some method of objects created to access only the name of the model, without its parameters. Or will I have to create an external list with the names for it?

I use:

for model in models:
   scores = cross_val_score(model, X, y, cv=5)
   print(f'Name model: {model} , Mean score: {scores.mean()}')
But I obtain the name with the parameters:

Name model: model = LinearSVC(), Mean score: 0.8066782865537986
In fact I want to get the information this way:

Name Model: LinearSVC, Mean Score: 0.8066782865537986
Thanks!

A:

<code>
import numpy as np
import pandas as pd
from sklearn.svm import LinearSVC
model = LinearSVC()
</code>
model_name = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.linear_model import LinearRegression
import sklearn
from sklearn.svm import LinearSVC

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            model = LinearRegression()
        elif test_case_id == 2:
            model = LinearSVC()
        return model

    def generate_ans(data):
        model = data
        model_name = type(model).__name__
        return model_name

    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
from sklearn.svm import LinearSVC
model = test_input
[insert]
result = model_name
"""

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:

Given the following example:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline
import pandas as pd

pipe = Pipeline([
    ("tf_idf", TfidfVectorizer()),
    ("nmf", NMF())
])

data = pd.DataFrame([["Salut comment tu vas", "Hey how are you today", "I am okay and you ?"]]).T
data.columns = ["test"]

pipe.fit_transform(data.test)
I would like to get intermediate data state in scikit learn pipeline corresponding to tf_idf output (after fit_transform on tf_idf but not NMF) or NMF input. Or to say things in another way, it would be the same than to apply

TfidfVectorizer().fit_transform(data.test)
I know pipe.named_steps["tf_idf"] ti get intermediate transformer, but I can't get data, only parameters of the transformer with this method.

A:

<code>
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline
import pandas as pd

data = load_data()

pipe = Pipeline([
    ("tf_idf", TfidfVectorizer()),
    ("nmf", NMF())
])
</code>
tf_idf_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

pipe.fit_transform(data.test)
tf_idf_out = pipe.named_steps['tf_idf'].transform(data.test)

import numpy as np
import pandas as pd
import copy
import tokenize, io
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = pd.DataFrame(
                [
                    [
                        "Salut comment tu vas",
                        "Hey how are you today",
                        "I am okay and you ?",
                    ]
                ]
            ).T
            data.columns = ["test"]
        return data

    def generate_ans(data):
        data = data
        pipe = Pipeline([("tf_idf", TfidfVectorizer()), ("nmf", NMF())])
        pipe.fit_transform(data.test)
        tf_idf_out = pipe.named_steps["tf_idf"].transform(data.test)
        return tf_idf_out

    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.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 TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline
data = test_input
pipe = Pipeline([
    ("tf_idf", TfidfVectorizer()),
    ("nmf", NMF())
])
[insert]
result = tf_idf_out
"""

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 "TfidfVectorizer" not in tokens


Prompt:
Problem:

I have encountered a problem that, I want to get the intermediate result of a Pipeline instance in sklearn.
However, for example, like this code below,
I don't know how to get the intermediate data state of the tf_idf output, which means, right after fit_transform method of tf_idf, but not nmf.

pipe = Pipeline([
    ("tf_idf", TfidfVectorizer()),
    ("nmf", NMF())
])

data = pd.DataFrame([["Salut comment tu vas", "Hey how are you today", "I am okay and you ?"]]).T
data.columns = ["test"]

pipe.fit_transform(data.test)

Or in another way, it would be the same than to apply
TfidfVectorizer().fit_transform(data.test)
pipe.named_steps["tf_idf"] ti can get the transformer tf_idf, but yet I can't get data.
Can anyone help me with that?

A:

<code>
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline
import pandas as pd

data = load_data()

pipe = Pipeline([
    ("tf_idf", TfidfVectorizer()),
    ("nmf", NMF())
])
</code>
tf_idf_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

pipe.fit_transform(data.test)
tf_idf_out = pipe.named_steps['tf_idf'].transform(data.test)

import numpy as np
import pandas as pd
import copy
import tokenize, io
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = pd.DataFrame(
                [
                    [
                        "Salut comment tu vas",
                        "Hey how are you today",
                        "I am okay and you ?",
                    ]
                ]
            ).T
            data.columns = ["test"]
        return data

    def generate_ans(data):
        data = data
        pipe = Pipeline([("tf_idf", TfidfVectorizer()), ("nmf", NMF())])
        pipe.fit_transform(data.test)
        tf_idf_out = pipe.named_steps["tf_idf"].transform(data.test)
        return tf_idf_out

    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.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 TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline
data = test_input
pipe = Pipeline([
    ("tf_idf", TfidfVectorizer()),
    ("nmf", NMF())
])
[insert]
result = tf_idf_out
"""

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 "TfidfVectorizer" not in tokens


Prompt:
Problem:

Given the following example:

from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
import pandas as pd

pipe = Pipeline(steps=[
    ('select', SelectKBest(k=2)),
    ('clf', LogisticRegression())]
)

pipe.fit(data, target)
I would like to get intermediate data state in scikit learn pipeline corresponding to 'select' output (after fit_transform on 'select' but not LogisticRegression). Or to say things in another way, it would be the same than to apply

SelectKBest(k=2).fit_transform(data, target)
Any ideas to do that?

A:

<code>
import numpy as np
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
import pandas as pd

data, target = load_data()

pipe = Pipeline(steps=[
    ('select', SelectKBest(k=2)),
    ('clf', LogisticRegression())]
)
</code>
select_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

select_out = pipe.named_steps['select'].fit_transform(data, target)

import numpy as np
import copy
import tokenize, io
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
import sklearn
from sklearn.datasets import load_iris

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            iris = load_iris()
        return iris.data, iris.target

    def generate_ans(data):
        data, target = data
        pipe = Pipeline(
            steps=[("select", SelectKBest(k=2)), ("clf", LogisticRegression())]
        )
        select_out = pipe.named_steps["select"].fit_transform(data, target)
        return select_out

    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
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
data, target = test_input
pipe = Pipeline(steps=[
    ('select', SelectKBest(k=2)),
    ('clf', LogisticRegression())]
)
[insert]
result = select_out
"""

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 "SelectKBest" not in tokens


Prompt:
Problem:

Say that I want to train BaggingClassifier that uses DecisionTreeClassifier:

dt = DecisionTreeClassifier(max_depth = 1)
bc = BaggingClassifier(dt, n_estimators = 20, max_samples = 0.5, max_features = 0.5)
bc = bc.fit(X_train, y_train)
I would like to use GridSearchCV to find the best parameters for both BaggingClassifier and DecisionTreeClassifier (e.g. max_depth from DecisionTreeClassifier and max_samples from BaggingClassifier), what is the syntax for this? Besides, you can just use the default arguments of GridSearchCV.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.ensemble import BaggingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier

X_train, y_train = load_data()
assert type(X_train) == np.ndarray
assert type(y_train) == np.ndarray
X_test = X_train
param_grid = {
    'base_estimator__max_depth': [1, 2, 3, 4, 5],
    'max_samples': [0.05, 0.1, 0.2, 0.5]
}
dt = DecisionTreeClassifier(max_depth=1)
bc = BaggingClassifier(dt, n_estimators=20, max_samples=0.5, max_features=0.5)
</code>
solve this question with example variable `clf` and put result in `proba`
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.ensemble import BaggingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
import sklearn
from sklearn.datasets import make_classification

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(
                n_samples=30, n_features=4, n_redundant=0, random_state=42
            )
        elif test_case_id == 2:
            X, y = make_classification(
                n_samples=30, n_features=4, n_redundant=0, random_state=24
            )
        return X, y

    def generate_ans(data):
        X_train, y_train = data
        X_test = X_train
        param_grid = {
            "estimator__max_depth": [1, 2, 3, 4, 5],
            "max_samples": [0.05, 0.1, 0.2, 0.5],
        }
        dt = DecisionTreeClassifier(max_depth=1, random_state=42)
        bc = BaggingClassifier(
            dt, n_estimators=20, max_samples=0.5, max_features=0.5, random_state=42
        )
        clf = GridSearchCV(bc, param_grid)
        clf.fit(X_train, y_train)
        proba = clf.predict_proba(X_test)
        return proba

    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
from sklearn.ensemble import BaggingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
X_train, y_train = test_input
X_test = X_train
param_grid = {
    'estimator__max_depth': [1, 2, 3, 4, 5],
    'max_samples': [0.05, 0.1, 0.2, 0.5]
}
dt = DecisionTreeClassifier(max_depth=1, random_state=42)
bc = BaggingClassifier(dt, n_estimators=20, max_samples=0.5, max_features=0.5, random_state=42)
[insert]
proba = clf.predict_proba(X_test)
result = proba
"""

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:

When trying to fit a Random Forest Regressor model with y data that looks like this:

[  0.00000000e+00   1.36094276e+02   4.46608221e+03   8.72660888e+03
   1.31375786e+04   1.73580193e+04   2.29420671e+04   3.12216341e+04
   4.11395711e+04   5.07972062e+04   6.14904935e+04   7.34275322e+04
   7.87333933e+04   8.46302456e+04   9.71074959e+04   1.07146672e+05
   1.17187952e+05   1.26953374e+05   1.37736003e+05   1.47239359e+05
   1.53943242e+05   1.78806710e+05   1.92657725e+05   2.08912711e+05
   2.22855152e+05   2.34532982e+05   2.41391255e+05   2.48699216e+05
   2.62421197e+05   2.79544300e+05   2.95550971e+05   3.13524275e+05
   3.23365158e+05   3.24069067e+05   3.24472999e+05   3.24804951e+05
And X data that looks like this:

[ 735233.27082176  735234.27082176  735235.27082176  735236.27082176
  735237.27082176  735238.27082176  735239.27082176  735240.27082176
  735241.27082176  735242.27082176  735243.27082176  735244.27082176
  735245.27082176  735246.27082176  735247.27082176  735248.27082176
With the following code:

regressor = RandomForestRegressor(n_estimators=150, min_samples_split=1.0, random_state=42)
rgr = regressor.fit(X,y)
I get this error:

ValueError: Number of labels=600 does not match number of samples=1
X data has only one feature and I assume one of my sets of values is in the wrong format but its not too clear to me from the documentation.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor

X, y, X_test = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
assert type(X_test) == np.ndarray
</code>
solve this question with example variable `regressor` and put prediction in `predict`
BEGIN SOLUTION
<code>

regressor = RandomForestRegressor(n_estimators=150, min_samples_split=1.0, random_state=42)
regressor.fit(X.reshape(-1, 1), y)

import numpy as np
import copy
from sklearn.ensemble import RandomForestRegressor
import sklearn
from sklearn.datasets import make_regression

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_regression(
                n_samples=100,
                n_features=1,
                n_informative=1,
                bias=150.0,
                noise=30,
                random_state=42,
            )
        return X.flatten(), y, X

    def generate_ans(data):
        X, y, X_test = data
        regressor = RandomForestRegressor(
            n_estimators=150, min_samples_split=1.0, random_state=42
        )
        regressor.fit(X.reshape(-1, 1), y)
        predict = regressor.predict(X_test)
        return predict

    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
from sklearn.ensemble import RandomForestRegressor
X, y, X_test = test_input
[insert]
predict = regressor.predict(X_test)
result = predict
"""

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 trying to fit a Random Forest Regressor model with y data that looks like this:
[   0.00   1.36   4.46   8.72
   1.31   1.73   2.29   3.12
   4.11   5.07   6.14   7.34
   7.87   8.46   9.71   1.07
   1.17   1.26   1.37   1.47
   1.53   1.78   1.92   2.08
   2.22   2.34   2.41   2.48
   2.62   2.79   2.95   3.13
   3.23   3.24   3.24   3.24
And X data that looks like this:

[  233.176  234.270  235.270  523.176
  237.176  238.270  239.270  524.176
  241.176  242.270  243.270  524.176
  245.176  246.270  247.270  524.176
With the following code:

regressor = RandomForestRegressor(n_estimators=150, min_samples_split=1.0, random_state=42)
rgr = regressor.fit(X,y)
I get this error:

ValueError: Number of labels=600 does not match number of samples=1
X data has only one feature and I assume one of my sets of values is in the wrong format but its not too clear to me from the documentation.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor

X, y, X_test = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
assert type(X_test) == np.ndarray
</code>
solve this question with example variable `regressor` and put prediction in `predict`
BEGIN SOLUTION
<code>

regressor = RandomForestRegressor(n_estimators=150, min_samples_split=1.0, random_state=42)
regressor.fit(X.reshape(-1, 1), y)

import numpy as np
import copy
from sklearn.ensemble import RandomForestRegressor
import sklearn
from sklearn.datasets import make_regression

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_regression(
                n_samples=100,
                n_features=1,
                n_informative=1,
                bias=150.0,
                noise=30,
                random_state=42,
            )
        return X.flatten(), y, X

    def generate_ans(data):
        X, y, X_test = data
        regressor = RandomForestRegressor(
            n_estimators=150, min_samples_split=1.0, random_state=42
        )
        regressor.fit(X.reshape(-1, 1), y)
        predict = regressor.predict(X_test)
        return predict

    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
from sklearn.ensemble import RandomForestRegressor
X, y, X_test = test_input
[insert]
predict = regressor.predict(X_test)
result = predict
"""

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:

How can I pass a preprocessor to TfidfVectorizer? I made a function "preprocess" that takes a string and returns a preprocessed string then I set processor parameter to that function "preprocessor=preprocess", but it doesn't work. I've searched so many times, but I didn't found any example as if no one use it.
the preprocessor looks like
def preprocess(s):
    return s.upper()

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
</code>
solve this question with example variable `tfidf`
BEGIN SOLUTION
<code>

def preprocess(s):
    return s.upper()

tfidf = TfidfVectorizer(preprocessor=preprocess)

import tokenize, io

def generate_test_case(test_case_id):
    return None, None

def exec_test(result, ans):
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
[insert]
result = None
assert preprocess("asdfASDFASDFWEQRqwerASDFAqwerASDFASDF") == "ASDFASDFASDFWEQRQWERASDFAQWERASDFASDF"
assert preprocess == tfidf.preprocessor
"""

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 "TfidfVectorizer" in tokens