markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
Test out your code by calling your function and printing the result. | # call the function to calculate the fraud percentage
fraud_percentage = fraudulent_percentage(transaction_df)
print('Fraudulent percentage = ', fraud_percentage)
print('Total # of fraudulent pts: ', fraud_percentage*transaction_df.shape[0])
print('Out of (total) pts: ', transaction_df.shape[0])
| Fraudulent percentage = 0.001727485630620034
Total # of fraudulent pts: 492.0
Out of (total) pts: 284807
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
EXERCISE: Split into train/test datasetsIn this example, we'll want to evaluate the performance of a fraud classifier; training it on some training data and testing it on *test data* that it did not see during the training process. So, we'll need to split the data into separate training and test sets.Complete the `tra... | from sklearn import model_selection
# split into train/test
def train_test_split(transaction_df, train_frac= 0.7, seed=1):
'''Shuffle the data and randomly split into train and test sets;
separate the class labels (the column in transaction_df) from the features.
:param df: Dataframe of all credit car... | _____no_output_____ | MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
Test CellIn the cells below, I'm creating the train/test data and checking to see that result makes sense. The tests below test that the above function splits the data into the expected number of points and that the labels are indeed, class labels (0, 1). | # get train/test data
(train_features, train_labels), (test_features, test_labels) = train_test_split(transaction_df, train_frac=0.7)
train_labels
# manual test
# for a split of 0.7:0.3 there should be ~2.33x as many training as test pts
print('Training data pts: ', len(train_features))
print('Test data pts: ', len(te... | Training data pts: 199364
Test data pts: 85443
First item:
[ 1.2912400e+05 -1.9007544e-01 2.0332274e-01 -9.9623245e-01
-1.5969852e+00 3.1925786e+00 3.3569350e+00 2.8829932e-01
8.9500278e-01 -3.3002400e-01 -6.4690042e-01 -2.8278485e-01
2.3240909e-02 -4.2109159e-01 3.6824697e-01 -1.5846483e-01
-3.3475593e... | MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
--- ModelingNow that you've uploaded your training data, it's time to define and train a model!In this notebook, you'll define and train the SageMaker, built-in algorithm, [LinearLearner](https://sagemaker.readthedocs.io/en/stable/linear_learner.html). A LinearLearner has two main applications:1. For regression tasks i... | # import LinearLearner
from sagemaker import LinearLearner
# instantiate LinearLearner
predictor = LinearLearner(role=role,
instance_count=1,
instance_type='ml.c4.xlarge',
predictor_type = 'binary_classifier',
) | _____no_output_____ | MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
EXERCISE: Convert data into a RecordSet formatNext, prepare the data for a built-in model by converting the train features and labels into numpy array's of float values. Then you can use the [record_set function](https://sagemaker.readthedocs.io/en/stable/linear_learner.htmlsagemaker.LinearLearner.record_set) to forma... | # create RecordSet of training data
formatted_train_data = predictor.record_set(train=train_features, labels=train_labels, channel='train') | _____no_output_____ | MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
EXERCISE: Train the EstimatorAfter instantiating your estimator, train it with a call to `.fit()`, passing in the formatted training data. | %%time
# train the estimator on formatted training data
predictor.fit(formatted_train_data) | Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.
Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
EXERCISE: Deploy the trained modelDeploy your model to create a predictor. We'll use this to make predictions on our test data and evaluate the model. | %%time
# deploy and create a predictor
linear_predictor = predictor.deploy(initial_instance_count=1, instance_type='ml.t2.medium') | Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
--- Evaluating Your ModelOnce your model is deployed, you can see how it performs when applied to the test data.According to the deployed [predictor documentation](https://sagemaker.readthedocs.io/en/stable/linear_learner.htmlsagemaker.LinearLearnerPredictor), this predictor expects an `ndarray` of input features and r... | # test one prediction
test_x_np = test_features.astype('float32')
result = linear_predictor.predict(test_x_np[0])
print(result) | [label {
key: "predicted_label"
value {
float32_tensor {
values: 0.0
}
}
}
label {
key: "score"
value {
float32_tensor {
values: 0.0002751190622802824
}
}
}
]
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
Helper function for evaluationThe provided function below, takes in a deployed predictor, some test features and labels, and returns a dictionary of metrics; calculating false negatives and positives as well as recall, precision, and accuracy. | # code to evaluate the endpoint on test data
# returns a variety of model metrics
def evaluate(predictor, test_features, test_labels, verbose=True):
"""
Evaluate a model on a test set given the prediction endpoint.
Return binary classification metrics.
:param predictor: A prediction endpoint
:para... | _____no_output_____ | MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
Test ResultsThe cell below runs the `evaluate` function. The code assumes that you have a defined `predictor` and `test_features` and `test_labels` from previously-run cells. | print('Metrics for simple, LinearLearner.\n')
# get metrics for linear predictor
metrics = evaluate(linear_predictor,
test_features.astype('float32'),
test_labels,
verbose=True) # verbose means we'll print out the metrics
| Metrics for simple, LinearLearner.
prediction (col) 0.0 1.0
actual (row)
0.0 85282 26
1.0 36 99
Recall: 0.733
Precision: 0.792
Accuracy: 0.999
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
Delete the EndpointI've added a convenience function to delete prediction endpoints after we're done with them. And if you're done evaluating the model, you should delete your model endpoint! | # Deletes a precictor.endpoint
def delete_endpoint(predictor):
try:
boto3.client('sagemaker').delete_endpoint(EndpointName=predictor.endpoint)
print('Deleted {}'.format(predictor.endpoint))
except:
print('Already deleted: {}'.format(predictor.endpoint))
# delete the p... | The endpoint attribute has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
The endpoint attribute has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
--- Model ImprovementsThe default LinearLearner got a high accuracy, but still classified fraudulent and valid data points incorrectly. Specifically classifying more than 30 points as false negatives (incorrectly labeled, fraudulent transactions), and a little over 30 points as false positives (incorrectly labeled, val... | # specify an output path
prefix = 'creditcard'
output_path = 's3://{}/{}'.format(bucket, prefix)
# instantiate a LinearLearner
# tune the model for a higher recall
linear_recall = LinearLearner(role=role,
train_instance_count=1,
train_instance_type='ml.c4.xl... | train_instance_count has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
train_instance_type has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
Train the tuned estimatorFit the new, tuned estimator on the formatted training data. | %%time
# train the estimator on formatted training data
linear_recall.fit(formatted_train_data) | Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.
Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
Deploy and evaluate the tuned estimatorDeploy the tuned predictor and evaluate it.We hypothesized that a tuned model, optimized for a higher recall, would have fewer false negatives (fraudulent transactions incorrectly labeled as valid); did the number of false negatives get reduced after tuning the model? | %%time
# deploy and create a predictor
recall_predictor = linear_recall.deploy(initial_instance_count=1, instance_type='ml.t2.medium')
print('Metrics for tuned (recall), LinearLearner.\n')
# get metrics for tuned predictor
metrics = evaluate(recall_predictor,
test_features.astype('float32'),
... | Metrics for tuned (recall), LinearLearner.
prediction (col) 0.0 1.0
actual (row)
0.0 84317 991
1.0 20 115
Recall: 0.852
Precision: 0.104
Accuracy: 0.988
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
Delete the endpoint As always, when you're done evaluating a model, you should delete the endpoint. Below, I'm using the `delete_endpoint` helper function I defined earlier. | # delete the predictor endpoint
delete_endpoint(recall_predictor) | The endpoint attribute has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
The endpoint attribute has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
--- Improvement: Managing Class ImbalanceWe have a model that is tuned to get a higher recall, which aims to reduce the number of false negatives. Earlier, we discussed how class imbalance may actually bias our model towards predicting that all transactions are valid, resulting in higher false negatives and true negati... | # instantiate a LinearLearner
# include params for tuning for higher recall
# *and* account for class imbalance in training data
linear_balanced = LinearLearner(role=role,
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
... | train_instance_count has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
train_instance_type has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
EXERCISE: Train the balanced estimatorFit the new, balanced estimator on the formatted training data. | %%time
# train the estimator on formatted training data
linear_balanced.fit(formatted_train_data) | Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.
Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
EXERCISE: Deploy and evaluate the balanced estimatorDeploy the balanced predictor and evaluate it. Do the results match with your expectations? | %%time
# deploy and create a predictor
balanced_predictor = linear_balanced.deploy(initial_instance_count=1, instance_type='ml.t2.medium')
print('Metrics for balanced, LinearLearner.\n')
# get metrics for balanced predictor
metrics = evaluate(balanced_predictor,
test_features.astype('float32'),
... | Metrics for balanced, LinearLearner.
prediction (col) 0.0 1.0
actual (row)
0.0 84629 679
1.0 18 117
Recall: 0.867
Precision: 0.147
Accuracy: 0.992
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
Delete the endpoint When you're done evaluating a model, you should delete the endpoint. | # delete the predictor endpoint
delete_endpoint(balanced_predictor) | The endpoint attribute has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
The endpoint attribute has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
| MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
A note on metric variability: The above model is tuned for the best possible precision with recall fixed at about 90%. The recall is fixed at 90% during training, but may vary when we apply our trained model to a test set of data. --- Model DesignNow that you've seen how to tune and balance a LinearLearner. Create, tra... | %%time
# instantiate and train a LinearLearner
# include params for tuning for higher precision
# *and* account for class imbalance in training data
%%time
# deploy and evaluate a predictor
## IMPORTANT
# delete the predictor endpoint after evaluation
| _____no_output_____ | MIT | Payment_Fraud_Detection/Fraud_Detection_Exercise.ipynb | simonmijares/ML-case-studies |
****** 11. 튜플과 집합****** *** 1 튜플 활용법***- 튜플(Tuples): 순서있는 임의의 객체 모음 (시퀀스형)- 튜플은 변경 불가능(Immutable)- 시퀀스형이 가지는 다음 연산 모두 지원 - 인덱싱, 슬라이싱, 연결, 반복, 멤버쉽 테스트 1-1 튜플 연산 | t1 = () # 비어있는 튜플
t2 = (1,2,3) # 괄호 사용
t3 = 1,2,3 # 괄호가 없어도 튜플이 됨
print(type(t1), type(t2), type(t3))
# <type 'tuple'> <type 'tuple'> <type 'tuple'>
r1 = (1,) # 자료가 한 개일 때는 반드시 콤마가 있어야 한다.
r2 = 1, # 괄호는 없어도 콤마는 있어야 한다.
print(type(r1))
print(type(r2))
# <type 'tuple'>
# <type 'tuple'>
t = (1, 2, 3)
print(t ... | 2 1
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
1-2 패킹과 언패킹 - 패킹 (Packing): 하나의 튜플 안에 여러 개의 데이터를 넣는 작업 | t = 1, 2, 'hello'
print(t)
print(type(t)) | (1, 2, 'hello')
<class 'tuple'>
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- 언패킹 (Unpacking): 하나의 튜플에서 여러 개의 데이터를 한꺼번에 꺼내와 각각 변수에 할당하는 작업 | x, y, z = t | _____no_output_____ | MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- 리스트로도 비슷한 작업이 가능하지만, 단순 패킹/언패킹 작업만을 목적으로 한다면 튜플 사용 추천 | a = ['foo', 'bar', 4, 5]
[x, y, z, w] = a
print(x)
print(y)
print(z)
print(w)
print()
x, y, z, w = a
print(x)
print(y)
print(z)
print(w) | foo
bar
4
5
foo
bar
4
5
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- 튜플과 리스트와의 공통점 - 원소에 임의의 객체를 저장 - 시퀀스 자료형 - 인덱싱, 슬라이싱, 연결, 반복, 멤버쉽 테스트 연산 지원 - 리스트와 다른 튜플만의 특징 - 변경 불가능 (Immutable) - 튜플은 count와 index 외에 다른 메소드를 가지지 않는다. | T = (1, 2, 2, 3, 3, 4, 4, 4, 4, 5)
print(T.count(4))
print(T.index(1)) | 4
0
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- list() 와 tuple() 내장 함수를 사용하여 리스트와 튜플을 상호 변환할 수 있음 | T = (1, 2, 3, 4, 5)
L = list(T)
L[0] = 100
print(L)
T = tuple(L)
print(T) | [100, 2, 3, 4, 5]
(100, 2, 3, 4, 5)
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
1-3 튜플의 사용 용도 - 튜플을 사용하는 경우 1: 함수가 하나 이상의 값을 리턴하는 경우 | def calc(a, b):
return a+b, a*b
x, y = calc(5, 4) | _____no_output_____ | MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- 튜플을 사용하는 경우 2: 문자열 포멧팅 | print('id : %s, name : %s' % ('gslee', 'GangSeong')) | id : gslee, name : GangSeong
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- 튜플을 사용하는 경우 3: 고정된 값을 쌍으로 표현하는 경우 | d = {'one':1, 'two':2}
print(d.items())
# [('two', 2), ('one', 1)] | dict_items([('one', 1), ('two', 2)])
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
*** 2 집합 자료형***- set 내장 함수를 사용한 집합 자료 생성 - 변경 가능(Mutable)한 객체이다. - 각 원소간에 순서는 없다. - 각 원소는 중복될 수 없다. - [note] set은 컨네이너 자료형이지만 시퀀스 자료형은 아니다. 2-1 집합 자료형 생성 | a = set([1, 2, 3])
print(type(a))
print(a)
b = set((1, 2, 3))
print(type(b))
print(b)
c = set({'a':1, 'b':2, 'c':3})
print(type(c))
print(c)
d = set({'a':1, 'b':2, 'c':3}.values())
print(type(d))
print(d) | <class 'set'>
{1, 2, 3}
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- E-learning에서 언급하지 않았던 집합 만드는 방법 --> 꼭 기억하세요~ | e = {1, 2, 3, 4, 5}
print(type(e))
print(e) | <class 'set'>
{1, 2, 3, 4, 5}
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- set에는 동일한 자료가 중복해서 저장되지 않는다. 즉 중복이 자동으로 제거됨 | f = {1, 1, 2, 2, 3, 3}
g = set([1, 1, 2, 2, 3, 3])
print(f)
print(g) | {1, 2, 3}
{1, 2, 3}
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- set의 원소로는 변경 불가능(Immutable)한 것만 할당 가능하다. | print(set()) # 빈 set 객체 생성
print(set([1, 2, 3, 4, 5])) # 초기 값은 일반적으로 시퀀스 자료형인 리스트를 넣어준다.
print(set([1, 2, 3, 2, 3, 4])) # 중복된 원소는 한 나만 저장됨
print(set('abc')) # 문자열은 각 문자를 집합 원소로 지닌다.
print(set([(1, 2, 3), (4, 5, 6)])) # 각 튜플은 원소로 가질 수 있음
print(set([[1, 2, 3], [4,... | _____no_output_____ | MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- set의 기본 연산| set 연산 | 동일 연산자 | 내용 ||---------------------------|-------------|-----------------------------|| len(s) | | 원소의 개수 || x in s | | x가 집합 s의 원소인가? || x not in s | ... | A = set([1, 2, 3, 4, 5, 6, 7, 8, 9])
print(len(A)) # 집합의 원소의 수
print(5 in A) # 멤버십 테스트
print(10 not in A) # 멤버십 테스트 | 9
True
True
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
2-2 집합 자료형 메소드 - set의 주요 메소드 - 다음 연산은 원래 집합은 변경하지 않고 새로운 집합을 반환한다. | set 연산 | 동일 연산자 | 내용 ||---------------------------|-------------|-----------------------------|| s.issubset(t) | s <= t | s가 t의 부분집합인가? || s.issuperset(t) | s >= t | s가 t의... | B = set([4, 5, 6, 10, 20, 30])
C = set([10, 20, 30])
print(C.issubset(B)) # C가 B의 부분집합?
print(C <= B)
print(B.issuperset(C)) # B가 C를 포함하는 집합?
print(B >= C)
print()
A = set([1, 2, 3, 4, 5, 6, 7, 8, 9])
B = set([4, 5, 6, 10, 20, 30])
print(A.union(B)) # A와 B의 합집합
print(A | B)
print(A)
print(A.intersection(B)) # A와 ... | {1, 2, 3, 4, 5, 6, 7, 8, 9}
True
False
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- set은 시퀀스 자료형이 아니므로 인덱싱, 슬라이싱, 정렬 등을 지원하지 않는다. | A = set([1, 2, 3, 4, 5, 6, 7, 8, 9])
print(A[0])
print(A[1:4])
print(A.sort()) | _____no_output_____ | MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- 집합을 리스트나 튜플로 변경가능 - 집합에 인덱싱, 슬라이싱, 정렬 등을 적용하기 위해서는 리스트나 튜플로 변경한다. | print(list(A))
print(tuple(A)) | [1, 2, 3, 4, 5, 6, 7, 8, 9]
(1, 2, 3, 4, 5, 6, 7, 8, 9)
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- 하지만 집합에 for ~ in 연산은 적용 가능하다. | A = set([1, 2, 3, 4, 5, 6, 7, 8, 9])
for ele in A:
print(ele,end=" ") | 1 2 3 4 5 6 7 8 9 | MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
- set은 변경 가능(Mutable)한 자료 구조 객체 - 다음 메소드들은 set을 변경하는 집합 자료 구조 메소드들임| set 연산 | 동일 연산자 | 내용 ||---------------------------|-------------|-----------------------------|| s.update(t) | s &124;= t | s와 t의 합집합을 s에 저장 || s.intersection_update(t) | s &= t | s... | A = set([1, 2, 3, 4])
B = set([3, 4, 5, 6])
A.update(B) # A에 B 집합의 원소를 추가 시킴
print(A)
A.intersection_update([4,5,6,7,8]) # &=
print(A)
A.difference_update([6,7,8]) # -=
print(A)
A.symmetric_difference_update([5,6,7]) # ^=
print(A)
A.add(8) # 원소 추가
print(A)
A.remove(8) # 원소 제거
print(A)
A.remove(10) # 없는 원소를 제거하면 Key... | set()
| MIT | python3.6/.ipynb_checkpoints/python11-checkpoint.ipynb | gjbr5/python-link-e-learning |
Parameter estimation in the glyoxalase system of _Leishmania infantum_. Part of the publicationSousa Silva, M. , Ferreira, A.E.N., Tomás, A.M., Cordeiro, C., Ponces Freire, A. (2005) Quantitative assessment of the glyoxalase pathway in Leishmania infantum as a therapeutic target by modelling and computer simulation.... | %matplotlib inline
import stimator as st | _____no_output_____ | MIT | silva_et_al_2005_FEBS_J/parameter_estimation.ipynb | aeferreira/papers_repr_glyoxalases |
Glyoxalase pathway in *L. infantum* This example uses **real data for a real biochemical pathway**.The main difference here is that we can actually use **two** time courses of experimental measures | glos = """
# Example file for S-timator
title Glyoxalase system in L. Infantum
variables SDLTSH HTA # variables (the order matches the timecourse files)
#reactions (with stoichiometry and rate)
glx1 : HTA -> SDLTSH, rate = V1*HTA/(Km1 + HTA)
glx2 : SDLTSH ->, V2*SDLTSH/(Km2 + SDLTSH)
find V1 in [0.00001, 0.0001]
fi... | _____no_output_____ | MIT | silva_et_al_2005_FEBS_J/parameter_estimation.ipynb | aeferreira/papers_repr_glyoxalases |
DataThe data are two time courses with spectrophotometric measurements of SDL-TSH. | st.readTCs(['TSH2a.txt', 'TSH2b.txt']).plot(fig_size=(12,5)) | _____no_output_____ | MIT | silva_et_al_2005_FEBS_J/parameter_estimation.ipynb | aeferreira/papers_repr_glyoxalases |
Parameter estimation | best = mglos.estimate()
print (best)
best.plot(fig_size=(12,5)) | -- reading time courses -------------------------------
file C:\Users\tonho\Desktop\other_github_repos\papers_repr_glyoxalases\silva_et_al_2005_FEBS_J\TSH2a.txt:
244 time points, 2 variables
file C:\Users\tonho\Desktop\other_github_repos\papers_repr_glyoxalases\silva_et_al_2005_FEBS_J\TSH2b.txt:
347 time points, 2 vari... | MIT | silva_et_al_2005_FEBS_J/parameter_estimation.ipynb | aeferreira/papers_repr_glyoxalases |
Test Riksdagen SFS dokument * Denna [Jupyter Notebook](https://github.com/salgo60/open-data-examples/blob/master/Riksdagens%20dokument%20SFS.ipynb) * [KU anmälningar](https://github.com/salgo60/open-data-examples/blob/master/Riksdagens%20dokument%20KU-anm%C3%A4lningar.ipynb) * [Motioner](https://github.com/salgo60/... | from datetime import datetime
now = datetime.now()
print("Last run: ", datetime.now())
import urllib3, json
import pandas as pd
from tqdm.notebook import trange
http = urllib3.PoolManager()
pd.set_option("display.max.columns", None)
urlbase ="http://data.riksdagen.se/dokumentlista/?sok=&doktyp=SFS&utformat=json&st... | _____no_output_____ | MIT | Riksdagens dokument SFS.ipynb | salgo60/open-data-examples |
Gissning * regl-riksg verkar vara Reglemente för Riksgäldskontoret * regl-riksb är nog Riksbanken | dftot.debattnamn.value_counts()
ftot = dftot.sort_values(by='id', ascending=False)
dftot.info()
dftot.head(1000)
print("End run: ", datetime.now()) | _____no_output_____ | MIT | Riksdagens dokument SFS.ipynb | salgo60/open-data-examples |
Comparing 🌈 to 🌈Often, we'll want to directly compare two different Rainbows. A wrapper called `MultiRainbow` tries to make doing so a little simpler, by providing an interface to apply many of the familiar Rainbow methods to multiple objects at once. | from chromatic import *
a = SimulatedRainbow(signal_to_noise=1000).inject_transit()
b = SimulatedRainbow(signal_to_noise=np.sqrt(100*1000)).inject_transit()
c = SimulatedRainbow(signal_to_noise=100).inject_transit()
m = MultiRainbow([a, b, c], names=['noisy', 'noisier', 'noisiest'])
m.bin(R=4).plot(spacing=0.01)
m.imsh... | _____no_output_____ | MIT | docs/multi.ipynb | zkbt/chromatic |
m.animate_spectra() | _____no_output_____ | MIT | docs/multi.ipynb | zkbt/chromatic | |
Implement a Stack A very common interview question is to begin by just implementing a Stack! Try your best to implement your own stack!It should have the methods:* Check if its empty* Push a new item* Pop an item* Peek at the top item* Return the size | class Stack(object):
# Fill out the Stack Methods here
def __init__(self):
self.items = []
def isEmpty(self):
return len(self.items) == 0
def push(self, e):
self.items.append(e)
def pop(self):
return self.items.pop()
def size(self):
return len(self.items)
... | _____no_output_____ | MIT | code/algorithms/course_udemy_1/Stacks, Queues and Deques/Interview/Questions - PRACTICE/Implement a Stack .ipynb | vicb1/miscellaneous |
Lists in Python Welcome! This notebook will teach you about the lists in the Python Programming Language. By the end of this lab, you'll know the basics list operations in Python, including indexing, list operations and copy/clone list. Table of Contents About the Dataset ... | # Create a list
L = ["Michael Jackson", 10.1, 1982]
L | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
We can use negative and regular indexing with a list : | L[0],L[-3]
# Print the elements on each index
print('the same element using negative and positive indexing:\n Postive:',L[0],
'\n Negative:' , L[-3] )
print('the same element using negative and positive indexing:\n Postive:',L[1],
'\n Negative:' , L[-2] )
print('the same element using negative and positive indexing:... | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
List Content Lists can contain strings, floats, and integers. We can nest other lists, and we can also nest tuples and other data structures. The same indexing conventions apply for nesting: | # Sample List
Sample_list = ["Michael Jackson", 10.1, 1982,2j+3,True ,[1, 2], ("A", 1)]
Sample_list
Sample_list[1],Sample_list[-6]
Sample_list[2]
Sample_list[0:5]
Sample_list[-5:-1] | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
List Operations We can also perform slicing in lists. For example, if we want the last two elements, we use the following command: | # Sample List
L = ["Michael Jackson", 10.1,1982,"MJ",1]
L | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
# List slicing
L[3:5] | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- | |
We can use the method extend to add new elements to the list will be add at last: | # Use extend to add elements to list
L = [ "Michael Jackson", 10.2]
L.extend(['pop', 10])
L | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Another similar method is append. If we apply append instead of extend, we add one element to the list: | # Use append to add elements to list
L = [ "Michael Jackson", 10.2]
L.append(['pop', 10])
L | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Each time we apply a method, the list changes. If we apply extend we add two new elements to the list. The list L is then modified by adding two new elements: | # Use extend to add elements to list
L = [ "Michael Jackson", 10.2]
L.extend(['pop', 10])
L | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
If we append the list ['a','b'] we have one new element consisting of a nested list: | # Use append to add elements to list
L.append(['a','b'])
L | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
As lists are mutable, we can change them. For example, we can change the first element as follows: | # Change the element based on the index
A = ["disco", 10, 1.2]
print('Before change:', A)
A[0]
A[0] = 'hard rock' # Mutable
print('After change:', A) | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
We can also delete an element of a list using the del command: | # Delete the element based on the index
print('Before change:', A)
del(A[0])
print('After change:', A) | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
We can convert a string to a list using split. For example, the method split translates every group of characters separated by a space into an element in a list: | # Split the string, default is by space
'hard rock'.split() | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
We can use the split function to separate strings on a specific character. We pass the character we would like to split on into the argument, which in this case is a comma. The result is a list, and each element corresponds to a set of characters that have been separated by a comma: | # Split the string by comma
'A,B,C,D'.split(',') | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Copy and Clone List When we set one variable B equal to A; both A and B are referencing the same list in memory: | # Copy (copy by reference) the list A
A = ["hard rock", 10, 1.2]
B = A # copy by reference
print('A:', A)
print('B:', B) | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
id(A)
id(B) | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- | |
Initially, the value of the first element in B is set as hard rock. If we change the first element in A to banana, we get an unexpected side effect. As A and B are referencing the same list, if we change list A, then list B also changes. If we check the first element of B we get banana instead of hard rock: | # Examine the copy by reference
print('B[0]:', B[0])
A[0] = "banana"
A
print('B[0]:', B[0])
B | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
This is demonstrated in the following figure: You can clone list **A** by using the following syntax: | # Clone (clone by value) the list A
B = A[:]
B | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Variable **B** references a new copy or clone of the original list; this is demonstrated in the following figure: Now if you change A, B will not change: | print('B[0]:', B[0])
A[0] = "hard rock"
print('B[0]:', B[0])
A
B
li = list(range(25,40)) # hear 25 is Starting Element and 39 Is Ending Element
li
li.append(10.25)
li
li.clear()
li
li_1 = [10,20,30,'hi','hello',True,2.5]
li_1
li_2 = li_1.copy()
li_2
li_1
li_1.append(10)
li_1
li_1.count(10)
li
li.extend(li_1)
li
li_1
l... | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
|Methods|Description||--------|----------||**Append()**|it is used to add the element at last in a list||**clear()**| it is used to Clear the all the elemnts in a list||**copy()**| it is used to copy all the elements in to list||**count()**|We are counting perticular element is Reparting in list||**extend()**|Add Multi... | a = [[10,20,30],
[2.5,3.5,4.5],
[True,False,True]]
a
a[0]
a[0][1]
a[1]
a[2]=10
a | _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Quiz on List Create a list a_list, with the following elements 1, hello, [1,2,3] and True. | # Write your code below and press Shift+Enter to execute
| _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Double-click here for the solution.<!-- Your answer is below:a_list = [1, 'hello', [1, 2, 3] , True]a_list--> Find the value stored at index 1 of a_list. | # Write your code below and press Shift+Enter to execute
| _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Double-click here for the solution.<!-- Your answer is below:a_list[1]--> Retrieve the elements stored at index 1, 2 and 3 of a_list. | # Write your code below and press Shift+Enter to execute
| _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Double-click here for the solution.<!-- Your answer is below:a_list[1:4]--> Concatenate the following lists A = [1, 'a'] and B = [2, 1, 'd']: | # Write your code below and press Shift+Enter to execute
| _____no_output_____ | CC0-1.0 | Day-2/Task-2/PY0101EN-2-2-Lists.ipynb | reddyprasade/2020-PY-101- |
Przykład formatowania tekstu w notebooku Nagłówek 1 Nagłówek 2 Nagłówek 3Gdy piszemy jakiś tekst w snippet to może on być **pogrubiony**, *kursywa* albo ***jedno i drugie***Można również tworzyć w snippetach listy:- li1- li2Moga być one numerowane:1. li12. li3 Maginczne komendyMagiczne komendy to dodataki/usprawnie... | #lista magicznych komend wywoujemy przy użyciu poniższej komendy
%lsmagic
#gdy trzeba doinstalowaać jakieś biblioteki zewnętrzene to można to zrobić przy użyciu takiej metody
####Wykonywanie polceń systemu operacyjnego
# instalacja dodatkowej biblioteki
!pip install pydotplus
x = 1.2342532345
x
# zmieni liczbę cyfr ... | _____no_output_____ | MIT | Dzien01/01-jupyter-intro.ipynb | gitgeoman/PAD2022 |
Import the data | import pandas as pd
import numpy as np
import networkx as nx
import statsmodels
import statsmodels.api as sm
import scipy.stats as stats
import matplotlib.pyplot as plt
# import the csv file with JUST the politicians post
comDB = pd.read_csv(r"/Users/tassan-mazzoccoarthur/Desktop/NETWORK SCIENCE (MOD. B)/1_Project... | _____no_output_____ | CC0-1.0 | Post_Group/com_matrix_and_network_creation.ipynb | Lally09-fx/net2020 |
Matrix creation | ## Matrix creation with parties, politicians, posts
#Setting variables
size_df= len(df)
parties=df['p_PARTITO'].unique().tolist()
pol=df['p_politician'].unique().tolist()
post=df['p_id'].unique().tolist()
#Matrix shape= root/party/party.....pol/pol/pol/...../post/post/.../com/com
total_size=1+len(parties)+len(pol)+len... | (88393, 88393)
| CC0-1.0 | Post_Group/com_matrix_and_network_creation.ipynb | Lally09-fx/net2020 |
Topic matrix: | #connect comments together on same topic -> only post and com matrix
topic=df['c_topic'].unique().tolist()
#Matrix creation
#topic/topic/...com/com
total_size=len(topic)+size_df
shift_size=len(topic)
topic_matrix = np.identity(total_size,dtype=int)
for i in range(size_df):
index_topic=topic.index(df['c_topic'][i])... | _____no_output_____ | CC0-1.0 | Post_Group/com_matrix_and_network_creation.ipynb | Lally09-fx/net2020 |
Adding attributes | import math
#create a dictionnary of attributes to update a networkx graph
# you should change the elements of the "attributes" array to select the attributes you want
def create_attributes_dict_com(pandas_df, index_shift): #index_shift -> value to shift the index of the nodes we focus our attention on
#for example: i... | _____no_output_____ | CC0-1.0 | Post_Group/com_matrix_and_network_creation.ipynb | Lally09-fx/net2020 |
Some cheks | import networkx as nx
#First let's try with a small matrix
m_trunc=m[:40000,:40000]
np.set_printoptions(linewidth=200)
print(m_trunc)
G_trunc = nx.from_numpy_matrix(m_trunc)
nx.write_graphml(G_trunc, "com_attributes_networkx_export.graphml") | _____no_output_____ | CC0-1.0 | Post_Group/com_matrix_and_network_creation.ipynb | Lally09-fx/net2020 |
Make your own heatmap based on Strava activitiesThis notebook shows you how to create your own heatmap based on your Strava activities.You need to create a Strava API application in order to use their API. Follow the instructions on this page to create your app: After setting up the app, note down the following inform... | !pip install stravaio folium
import os
import logging
import json
import urllib
import requests
import folium
from stravaio import StravaIO
# Paste your client id and client secret here.
STRAVA_CLIENT_ID = "ENTER-YOUR-CLIENT-ID"
STRAVA_CLIENT_SECRET = "ENTER-YOUR-CLIENT-SECRET" | _____no_output_____ | MIT | examples/make_your_own.ipynb | rafaelschlatter/strava-heatmap |
Authorization with StravaThe cell below creates the proper authorization link using the Stravaio Python library, which is used later to retrieve activities.It is important to run this cell, just pasting the access_token from your Strava settings will not work, because Stravaio needs to be authorized.- Run the cell bel... | params_oauth = {
"client_id": STRAVA_CLIENT_ID,
"response_type": "code",
"redirect_uri": f"http://localhost:8000/authorization_successful",
"scope": "read,profile:read_all,activity:read",
"state": 'https://github.com/sladkovm/strava-http', # Sladkovm is the author of the Stravaio library
"approv... | _____no_output_____ | MIT | examples/make_your_own.ipynb | rafaelschlatter/strava-heatmap |
The following cell retrieves an access token using the authorization code. That access token can then be used to retrieve Strava data. | payload = {
"client_id": STRAVA_CLIENT_ID,
"client_secret": STRAVA_CLIENT_SECRET,
"grant_type": "authorization_code",
"code": AUTHORIZATION_CODE,
}
response = requests.request(
"POST", "https://www.strava.com/api/v3/oauth/token", data=payload
)
response = json.loads(response.text)
TOKEN = response... | _____no_output_____ | MIT | examples/make_your_own.ipynb | rafaelschlatter/strava-heatmap |
Part Two: Scaling & Normalization | from IPython.display import HTML
from IPython.display import Image
Image(url= "https://miro.medium.com/max/3316/1*yR54MSI1jjnf2QeGtt57PA.png")
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document )... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
Table of Contents - 1. [Introduction](Introduction) - 1.1 [Abstract](abstract) - 1.2 [Importing Libraries](importing_libraries)- 2. [Data Scaling](data_scaling) - 2.1 [Standardization](standardization) - 2.2 [Normalization](normalization) - 2.3 [The Big Question – Normalize or Standardize?](the_bi... | # modules we'll use
import pandas as pd
import numpy as np
# for Box-Cox Transformation
from scipy import stats
# for min_max scaling
from sklearn import preprocessing
from mlxtend.preprocessing import minmax_scaling
# plotting modules
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from ast... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
2.0 Data Scaling Why Should we Use Feature Scaling?The first question we need to address – why do we need to scale the variables in our dataset? Some machine learning algorithms are sensitive to feature scaling while others are virtually invariant to it. Machine learning models learn a mapping from input variables to... | # generate 1000 data points randomly drawn from an exponential distribution
original_data = np.random.exponential(size=1000)
# mix-max scale the data between 0 and 1
scaled_data = minmax_scaling(original_data, columns=[0])
# plot both together to compare
fig, ax = plt.subplots(1,2)
sns.distplot(original_data, ax=ax[0... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
2.2 Normalization **Normalization:** Change in the shape of the distribution of data.Normalization scales each input variable separately to the range 0-1, which is the range for floating-point values where we have the most precision. Normalization requires that you know or are able to accurately estimate the minimum ... | # normalize the exponential data with boxcox
normalized_data = stats.boxcox(original_data)
# plot both together to compare
fig, ax=plt.subplots(1,2)
sns.distplot(original_data, ax=ax[0])
ax[0].set_title("Original Data")
sns.distplot(normalized_data[0], ax=ax[1])
ax[1].set_title("Normalized data") | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
2.3 The Big Question – Normalize or Standardize? Normalization vs. standardization is an eternal question among machine learning newcomers. Let me elaborate on the answer in this section.- Normalization is good to use when you know that the distribution of your data does not follow a Gaussian distribution. This can be... | #create columns of various distributions
df = pd.DataFrame({
'beta': np.random.beta(5, 1, 1000) * 60, # beta
'exponential': np.random.exponential(10, 1000), # exponential
'normal_p': np.random.normal(10, 2, 1000), # normal platykurtic
'normal_l': np.random.normal(10, 10, 1000), # normal... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
After defining the distributions, lets visualize them | # plot original distribution plot
fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 8))
ax1.set_title('Original Distributions')
sns.kdeplot(df['beta'], ax=ax1)
sns.kdeplot(df['exponential'], ax=ax1)
sns.kdeplot(df['normal_p'], ax=ax1)
sns.kdeplot(df['normal_l'], ax=ax1)
sns.kdeplot(df['bimodal'], ax=ax1);
df.describe()
... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
As we can clearly see from the statistics and the plots, all values are in the same ball park. But what happens if we disturb this by adding a feature with much larger values. 2.4.2 Adding a Feature with Much Larger Values This feature could be home prices, for example.[Back to Top](Introduction) | normal_big = np.random.normal(1000000, 10000, (1000,1)) # normal distribution of large values
df['normal_big'] = normal_big
col_names.append('normal_big')
df['normal_big'].plot(kind='kde')
df.normal_big.mean() | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
We've got a normalish distribution with a mean near 1,000,0000. But if we put this on the same plot as the original distributions, you can't even see the earlier columns. | # plot original distribution plot with larger value feature
fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 8))
ax1.set_title('Original Distributions')
sns.kdeplot(df['beta'], ax=ax1)
sns.kdeplot(df['exponential'], ax=ax1)
sns.kdeplot(df['normal_p'], ax=ax1)
sns.kdeplot(df['normal_l'], ax=ax1)
sns.kdeplot(df['bimodal'... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
The new, high-value distribution is way to the right. And here's a plot of the values. | df.plot() | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
2.4.3 MinMaxScaler MinMaxScaler subtracts the column mean from each value and then divides by the range.[Back to Top](Introduction) | mm_scaler = preprocessing.MinMaxScaler()
df_mm = mm_scaler.fit_transform(df)
df_mm = pd.DataFrame(df_mm, columns=col_names)
fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 8))
ax1.set_title('After MinMaxScaler')
sns.kdeplot(df_mm['beta'], ax=ax1)
sns.kdeplot(df_mm['exponential'], ax=ax1)
sns.kdeplot(df_mm['normal_p'... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
Notice how the shape of each distribution remains the same, but now the values are between 0 and 1. Our feature with much larger values was brought into scale with our other features. 2.4.4 StandardScaler StandardScaler is scales each column to have 0 mean and unit variance.[Back to Top](Introduction) | s_scaler = preprocessing.StandardScaler()
df_s = s_scaler.fit_transform(df)
df_s = pd.DataFrame(df_s, columns=col_names)
fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 8))
ax1.set_title('After StandardScaler')
sns.kdeplot(df_s['beta'], ax=ax1)
sns.kdeplot(df_s['exponential'], ax=ax1)
sns.kdeplot(df_s['normal_p'], a... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
You can see that all features now have 0 mean. | df_s.describe() | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
2.4.5 RobustScaler RobustScaler subtracts the column median and divides by the interquartile range.[Back to Top](Introduction) | r_scaler = preprocessing.RobustScaler()
df_r = r_scaler.fit_transform(df)
df_r = pd.DataFrame(df_r, columns=col_names)
fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 8))
ax1.set_title('After RobustScaler')
sns.kdeplot(df_r['beta'], ax=ax1)
sns.kdeplot(df_r['exponential'], ax=ax1)
sns.kdeplot(df_r['normal_p'], ax=ax... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
Although the range of values for each feature is much smaller than for the original features, it's larger and varies more than for MinMaxScaler. The bimodal distribution values are now compressed into two small groups. Standard and RobustScalers have pretty much the same ranges. 2.4.6 Normalizer Note that normalizer o... | n_scaler = preprocessing.Normalizer()
df_n = n_scaler.fit_transform(df)
df_n = pd.DataFrame(df_n, columns=col_names)
fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 8))
ax1.set_title('After Normalizer')
sns.kdeplot(df_n['beta'], ax=ax1)
sns.kdeplot(df_n['exponential'], ax=ax1)
sns.kdeplot(df_n['normal_p'], ax=ax1)
s... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
Normalizer also moved the features to similar scales. Notice that the range for our much larger feature's values is now extremely small and clustered around .9999999999. 2.4.7 Combined Plot Let's look at our original and transformed distributions together. We'll exclude Normalizer because you generally want to tranfor... | # Combined plot.
fig, (ax0, ax1, ax2, ax3) = plt.subplots(ncols=4, figsize=(20, 8))
ax0.set_title('Original Distributions')
sns.kdeplot(df['beta'], ax=ax0)
sns.kdeplot(df['exponential'], ax=ax0)
sns.kdeplot(df['normal_p'], ax=ax0)
sns.kdeplot(df['normal_l'], ax=ax0)
sns.kdeplot(df['bimodal'], ax=ax0)
sns.kdeplot(df... | _____no_output_____ | Unlicense | Data Science/Data_Preprocessing/Notebooks/INFO7390_Assignment_3_Mini_Project_One_Part_Two.ipynb | RushabhNisher/Data |
The below derives fromhttps://github.com/yihui/knitr/blob/master/inst/examples/knitr-spin.RThis is a special R script which can be used to generate a report. You canwrite normal text in roxygen comments.First we set up some options (you do not have to do this): | library(knitr)
opts_chunk$set(fig.path = 'figure/silk-') | _____no_output_____ | MIT | tests/notebooks/mirror/script_to_ipynb/knitr-spin.ipynb | kiendang/jupytext |
The report begins here. | # boring examples as usual
set.seed(123)
x = rnorm(5)
mean(x) | _____no_output_____ | MIT | tests/notebooks/mirror/script_to_ipynb/knitr-spin.ipynb | kiendang/jupytext |
You can not use here the special syntax {{code}} to embed inline expressions, e.g. | {{mean(x) + 2}} | _____no_output_____ | MIT | tests/notebooks/mirror/script_to_ipynb/knitr-spin.ipynb | kiendang/jupytext |
is the mean of x plus 2.The code itself may contain braces, but these are not checked. Thus,perfectly valid (though very strange) R code such as `{{2 + 3}} - {{4 - 5}}`can lead to errors because `2 + 3}} - {{4 - 5` will be treated as inline code.Now we continue writing the report. We can draw plots as well. | par(mar = c(4, 4, .1, .1)); plot(x) | _____no_output_____ | MIT | tests/notebooks/mirror/script_to_ipynb/knitr-spin.ipynb | kiendang/jupytext |
Actually you do not have to write chunk options, in which case knitr will usedefault options. For example, the code below has no options attached: | var(x)
quantile(x) | _____no_output_____ | MIT | tests/notebooks/mirror/script_to_ipynb/knitr-spin.ipynb | kiendang/jupytext |
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