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 |
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TrainingTrain the network on random samples from the data. Try adjusting the epochs and watch the training performance closely using different models. | %matplotlib notebook
from matplotlib import cm
EPOCHS = 200000
max_accuracy = 0
fig, ax = plt.subplots(num='Training')
scatter = ax.scatter(*inputs.T, 2)
plt.show()
for epoch in range(1, EPOCHS + 1):
sample_index = np.random.randint(0, len(targets))
MLP.adapt(inputs[sample_index], targets[sample_index])
... | _____no_output_____ | MIT | sheet_08/sheet_08_machine-learning_solution.ipynb | ArielMant0/ml2018 |
Evaluation | %matplotlib inline
fig, ax = plt.subplots(nrows=2, ncols=2)
ax[0, 0].scatter(*inputs.T, 2, c=outputs, cmap='RdYlBu')
ax[0, 0].set_title('Continuous Classification')
ax[0, 1].set_title('Discretized Classification')
ax[0, 1].scatter(*inputs.T, 2, c=np.round(outputs), cmap='RdYlBu')
ax[1, 0].set_title('Original Labels')
a... | _____no_output_____ | MIT | sheet_08/sheet_08_machine-learning_solution.ipynb | ArielMant0/ml2018 |
ResultsDocument your results in the following cell. We are interested in which network configurations you tried and what accuracies they resulted in. Did you run into problems during training? Was it steady or did it get stuck? Did you recognize anything about the training process? How could we get better results? Tel... | %matplotlib inline
import numpy as np
from numpy.random import uniform
from scipy.interpolate import Rbf
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
def func(x, y):
"""
This is the example function that should be fitted.
Its shape could be described as two peaks close to
... | _____no_output_____ | MIT | sheet_08/sheet_08_machine-learning_solution.ipynb | ArielMant0/ml2018 |
Tem caracteres em chinês? Pq eles pegam a maior distribuição do dataset???Tirado do Twitter? (Alguns nomes/sobrenomes)O Dataset do Bert base inglês parecia mais organizadoCade o alfabeto?Tem muitas subwords | !pip install transformers
from transformers import AutoTokenizer # Or BertTokenizer
from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads
from transformers import AutoModel # or BertModel, for BERT without pretraining heads
model = AutoModelForPreTraining.from_pretra... | /usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
| MIT | BERTimbau.ipynb | Laelapz/Some_Tests |
The Binomial Distribution This notebook is part of [Bite Size Bayes](https://allendowney.github.io/BiteSizeBayes/), an introduction to probability and Bayesian statistics using Python.Copyright 2020 Allen B. DowneyLicense: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommo... | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print('Downloaded ' + local)
download('https://github.com/AllenDowney/BiteSizeBayes/raw/master/utils.py... | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
If everything we need is installed, the following cell should run with no error messages. | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
The Euro problem revisitedIn [a previous notebook](https://colab.research.google.com/github/AllenDowney/BiteSizeBayes/blob/master/07_euro.ipynb) I presented a problem from David MacKay's book, [*Information Theory, Inference, and Learning Algorithms*](http://www.inference.org.uk/mackay/itila/p0.html):> A statistical s... | from scipy.stats import binom
n = 2
p = 0.5
ks = np.arange(n+1)
a = binom.pmf(ks, n, p)
a | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
If we put this result in a Series, the result is the distribution of `k` for the given values of `n` and `p`. | pmf_k = pd.Series(a, index=ks)
pmf_k | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
The following function computes the binomial distribution for given values of `n` and `p`: | def make_binomial(n, p):
"""Make a binomial PMF.
n: number of spins
p: probability of heads
returns: Series representing a PMF
"""
ks = np.arange(n+1)
a = binom.pmf(ks, n, p)
pmf_k = pd.Series(a, index=ks)
return pmf_k | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
And here's what it looks like with `n=250` and `p=0.5`: | pmf_k = make_binomial(n=250, p=0.5)
pmf_k.plot()
plt.xlabel('Number of heads (k)')
plt.ylabel('Probability')
plt.title('Binomial distribution'); | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
The most likely value in this distribution is 125: | pmf_k.idxmax() | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
But even though it is the most likely value, the probability that we get exactly 125 heads is only about 5%. | pmf_k[125] | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
In MacKay's example, we got 140 heads, which is less likely than 125: | pmf_k[140] | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
In the article MacKay quotes, the statistician says, ‘If the coin were unbiased the chance of getting a result as extreme as that would be less than 7%’.We can use the binomial distribution to check his math. The following function takes a PMF and computes the total probability of values greater than or equal to `thre... | def prob_ge(pmf, threshold):
"""Probability of values greater than a threshold.
pmf: Series representing a PMF
threshold: value to compare to
returns: probability
"""
ge = (pmf.index >= threshold)
total = pmf[ge].sum()
return total | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Here's the probability of getting 140 heads or more: | prob_ge(pmf_k, 140) | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
It's about 3.3%, which is less than 7%. The reason is that the statistician includes all values "as extreme as" 140, which includes values less than or equal to 110, because 140 exceeds the expected value by 15 and 110 falls short by 15. The probability of values less than or equal to 110 is also 3.3%,so the total pro... | xs = np.arange(101) / 100
uniform = pd.Series(1, index=xs)
uniform /= uniform.sum() | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
We can use `binom.pmf` to compute the likelihood of the data for each possible value of $x$. | k = 140
n = 250
xs = uniform.index
likelihood = binom.pmf(k, n, p=xs) | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Now we can do the Bayesian update in the usual way, multiplying the priors and likelihoods, | posterior = uniform * likelihood | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Computing the total probability of the data, | total = posterior.sum()
total | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
And normalizing the posterior, | posterior /= total | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Here's what it looks like. | posterior.plot(label='Uniform')
plt.xlabel('Probability of heads (x)')
plt.ylabel('Probability')
plt.title('Posterior distribution, uniform prior')
plt.legend() | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
**Exercise:** Based on what we know about coins in the real world, it doesn't seem like every value of $x$ is equally likely. I would expect values near 50% to be more likely and values near the extremes to be less likely. In Notebook 7, we used a triangle prior to represent this belief about the distribution of $x$.... | ramp_up = np.arange(50)
ramp_down = np.arange(50, -1, -1)
a = np.append(ramp_up, ramp_down)
triangle = pd.Series(a, index=xs)
triangle /= triangle.sum() | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Update this prior with the likelihoods we just computed and plot the results. | # Solution
posterior2 = triangle * likelihood
total2 = posterior2.sum()
total2
# Solution
posterior2 /= total2
# Solution
posterior.plot(label='Uniform')
posterior2.plot(label='Triangle')
plt.xlabel('Probability of heads (x)')
plt.ylabel('Probability')
plt.title('Posterior distribution, uniform prior')
plt.legend()... | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
EvidenceFinally, let's get back to MacKay's question: do these data give evidence that the coin is biased rather than fair?I'll use a Bayes table to answer this question, so here's the function that makes one: | def make_bayes_table(hypos, prior, likelihood):
"""Make a Bayes table.
hypos: sequence of hypotheses
prior: prior probabilities
likelihood: sequence of likelihoods
returns: DataFrame
"""
table = pd.DataFrame(index=hypos)
table['prior'] = prior
table['likelihood'] = likeliho... | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Recall that data, $D$, is considered evidence in favor of a hypothesis, `H`, if the posterior probability is greater than the prior, that is, if$P(H|D) > P(H)$For this example, I'll call the hypotheses `fair` and `biased`: | hypos = ['fair', 'biased'] | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
And just to get started, I'll assume that the prior probabilities are 50/50. | prior = [0.5, 0.5] | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Now we have to compute the probability of the data under each hypothesis.If the coin is fair, the probability of heads is 50%, and we can compute the probability of the data (140 heads out of 250 spins) using the binomial distribution: | k = 140
n = 250
like_fair = binom.pmf(k, n, p=0.5)
like_fair | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
So that's the probability of the data, given that the coin is fair.But if the coin is biased, what's the probability of the data? Well, that depends on what "biased" means.If we know ahead of time that "biased" means the probability of heads is 56%, we can use the binomial distribution again: | like_biased = binom.pmf(k, n, p=0.56)
like_biased | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Now we can put the likelihoods in the Bayes table: | likes = [like_fair, like_biased]
make_bayes_table(hypos, prior, likes) | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
The posterior probability of `biased` is about 86%, so the data is evidence that the coin is biased, at least for this definition of "biased".But we used the data to define the hypothesis, which seems like cheating. To be fair, we should define "biased" before we see the data. Uniformly distributed biasSuppose "biase... | biased_uniform = uniform.copy()
biased_uniform[50] = 0
biased_uniform /= biased_uniform.sum() | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Now, to compute the probability of the data under this hypothesis, we compute the probability of the data for each value of $x$. | xs = biased_uniform.index
likelihood = binom.pmf(k, n, xs) | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
And then compute the total probability in the usual way: | like_uniform = np.sum(biased_uniform * likelihood)
like_uniform | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
So that's the probability of the data under the "biased uniform" hypothesis.Now we make a Bayes table that compares the hypotheses `fair` and `biased uniform`: | hypos = ['fair', 'biased uniform']
likes = [like_fair, like_uniform]
make_bayes_table(hypos, prior, likes) | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Using this definition of `biased`, the posterior is less than the prior, so the data are evidence that the coin is *fair*.In this example, the data might support the fair hypothesis or the biased hypothesis, depending on the definition of "biased". **Exercise:** Suppose "biased" doesn't mean every value of $x$ is equal... | biased_triangle = triangle.copy()
biased_triangle[50] = 0
biased_triangle /= biased_triangle.sum() | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Compute the total probability of the data under this definition of "biased" and use a Bayes table to compare it with the fair hypothesis.Is the data evidence that the coin is biased? | # Solution
like_triangle = np.sum(biased_triangle * likelihood)
like_triangle
# Solution
hypos = ['fair', 'biased triangle']
likes = [like_fair, like_triangle]
make_bayes_table(hypos, prior, likes)
# Solution
# For this definition of "biased",
# the data are slightly in favor of the fair hypothesis. | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Bayes factorIn the previous section, we used a Bayes table to see whether the data are in favor of the fair or biased hypothesis.I assumed that the prior probabilities were 50/50, but that was an arbitrary choice. And it was unnecessary, because we don't really need a Bayes table to say whether the data favor one hyp... | like_fair, like_biased | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
Under the biased uniform definition, the likelihood of the fair hypothesis is higher. | like_fair, like_uniform | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
The ratio of these likelihoods tells us which hypothesis the data support.If the ratio is less than 1, the data support the second hypothesis: | like_fair / like_biased | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
If the ratio is greater than 1, the data support the first hypothesis: | like_fair / like_uniform | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
This likelihood ratio is called a [Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor); it provides a concise way to present the strength of a dataset as evidence for or against a hypothesis. SummaryIn this notebook I introduced the binomial disrtribution and used it to solve the Euro problem more efficiently.Th... | def pmf_mean(pmf):
"""Compute the mean of a PMF.
pmf: Series representing a PMF
return: float
"""
return np.sum(pmf.index * pmf)
# Solution
xs = np.linspace(0.1, 0.4)
prior = pd.Series(1, index=xs)
prior /= prior.sum()
# Solution
likelihood = xs**2 + (1-xs)**2
# Solution
posterior = pri... | _____no_output_____ | MIT | solutions/12_binomial.ipynb | jonathonfletcher/BiteSizeBayes |
1.1. Introducing IPython and the Jupyter Notebook | print("Hello world!")
2 + 2
_ * 3
!ls
%lsmagic
%%writefile test.txt
Hello world!
# Let's check what this file contains.
with open('test.txt', 'r') as f:
print(f.read())
%run?
from IPython.display import HTML, SVG, YouTubeVideo
HTML('''
<table style="border: 2px solid black;">
''' +
''.join(['<tr>' +
... | _____no_output_____ | MIT | chapter01_basic/01_notebook.ipynb | aaazzz640/cookbook-2nd-code |
DataLit Homework Assignment Week 4Historical sales data from 45 stores. This dataset comes from KAGGLE (https://www.kaggle.com/manjeetsingh/retaildataset). | # Stores
# Contains anonymized information about the 45 stores, indicating the type and size of store.
stores = 'dataset/stores data-set.csv'
# feature
# Contains additional data related to the store, department, and regional activity for the given dates.
feature = 'dataset/Features data set.csv'
# Sales
# Contains h... | _____no_output_____ | MIT | week4/.ipynb_checkpoints/Retail-Data-Analytics-checkpoint.ipynb | guillainbisimwa/Data-lit |
A/B testing, traffic shifting and autoscaling IntroductionIn this lab you will create an endpoint with multiple variants, splitting the traffic between them. Then after testing and reviewing the endpoint performance metrics, you will shift the traffic to one variant and configure it to autoscale. Table of Contents- [... | # please ignore warning messages during the installation
!pip install --disable-pip-version-check -q sagemaker==2.35.0
!conda install -q -y pytorch==1.6.0 -c pytorch
!pip install --disable-pip-version-check -q transformers==3.5.1
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format='re... | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
1. Create an endpoint with multiple variants Two models trained to analyze customer feedback and classify the messages into positive (1), neutral (0), and negative (-1) sentiments are saved in the following S3 bucket paths. These `tar.gz` files contain the model artifacts, which result from model training. | model_a_s3_uri = 's3://dlai-practical-data-science/models/ab/variant_a/model.tar.gz'
model_b_s3_uri = 's3://dlai-practical-data-science/models/ab/variant_b/model.tar.gz' | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Let's deploy an endpoint splitting the traffic between these two models 50/50 to perform A/B Testing. Instead of creating a PyTorch Model object and calling `model.deploy()` function, you will create an `Endpoint configuration` with multiple model variants. Here is the workflow you will follow to create an endpoint: 1... | inference_instance_type = 'ml.m5.large' | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Exercise 1Create an ECR URI using the `'PyTorch'` framework. Review other parameters of the image. | inference_image_uri = sagemaker.image_uris.retrieve(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
framework='pytorch', # Replace None
### END SOLUTION - DO NOT delete this comment for grading purposes
version='1.6.0',
instance_type=inference_instance_type,
region=region,
... | 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference:1.6.0-cpu-py3
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
1.2. Create Amazon SageMaker ModelsAmazon SageMaker Model includes information such as the S3 location of the model, the container image that can be used for inference with that model, the execution role, and the model name. Let's construct the model names. | import time
from pprint import pprint
timestamp = int(time.time())
model_name_a = '{}-{}'.format('a', timestamp)
model_name_b = '{}-{}'.format('b', timestamp) | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
You will use the following function to check if the model already exists in Amazon SageMaker. | def check_model_existence(model_name):
for model in sm.list_models()['Models']:
if model_name == model['ModelName']:
return True
return False | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Exercise 2Create an Amazon SageMaker Model based on the `model_a_s3_uri` data.**Instructions**: Use `sm.create_model` function, which requires the model name, Amazon SageMaker execution role and a primary container description (`PrimaryContainer` dictionary). The `PrimaryContainer` includes the S3 bucket location of t... | if not check_model_existence(model_name_a):
model_a = sm.create_model(
ModelName=model_name_a,
ExecutionRoleArn=role,
PrimaryContainer={
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
'ModelDataUrl': model_a_s3_uri, # Replace None
'Im... | {'ModelArn': 'arn:aws:sagemaker:us-east-1:299076282420:model/a-1638384450',
'ResponseMetadata': {'HTTPHeaders': {'content-length': '74',
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 01 Dec 2021 18:48:32 GMT',
... | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Exercise 3Create an Amazon SageMaker Model based on the `model_b_s3_uri` data.**Instructions**: Use the example in the cell above. | if not check_model_existence(model_name_b):
model_b = sm.create_model(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
ModelName=model_name_b,
ExecutionRoleArn=role,
### END SOLUTION - DO NOT delete this comment for grading purposes
PrimaryContainer={
... | {'ModelArn': 'arn:aws:sagemaker:us-east-1:299076282420:model/b-1638384450',
'ResponseMetadata': {'HTTPHeaders': {'content-length': '74',
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 01 Dec 2021 18:48:48 GMT',
... | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
1.3. Set up Amazon SageMaker production variantsA production variant is a packaged SageMaker Model combined with the configuration related to how that model will be hosted. You have constructed the model in the section above. The hosting resources configuration includes information on how you want that model to be hos... | from sagemaker.session import production_variant
variantA = production_variant(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
model_name=model_name_a, # Replace None
instance_type=inference_instance_type, # Replace None
### END SOLUTION - DO NOT delete this comment for grading pu... | {'ModelName': 'a-1638384450', 'InstanceType': 'ml.m5.large', 'InitialInstanceCount': 1, 'VariantName': 'VariantA', 'InitialVariantWeight': 50}
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Exercise 5Create an Amazon SageMaker production variant for the SageMaker Model with the `model_name_b`.**Instructions**: See the required arguments in the cell above. | variantB = production_variant(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
model_name=model_name_b, # Replace all None
instance_type=inference_instance_type, # Replace all None
initial_weight=50, # Replace all None
### END SOLUTION - DO NOT delete this comment for grading pu... | {'ModelName': 'b-1638384450', 'InstanceType': 'ml.m5.large', 'InitialInstanceCount': 1, 'VariantName': 'VariantB', 'InitialVariantWeight': 50}
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
1.4. Configure and create the endpointYou will use the following functions to check if the endpoint configuration and endpoint itself already exist in Amazon SageMaker. | def check_endpoint_config_existence(endpoint_config_name):
for endpoint_config in sm.list_endpoint_configs()['EndpointConfigs']:
if endpoint_config_name == endpoint_config['EndpointConfigName']:
return True
return False
def check_endpoint_existence(endpoint_name):
for endpoint in sm.lis... | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Create the endpoint configuration by specifying the name and pointing to the two production variants that you just configured that tell SageMaker how you want to host those models. | endpoint_config_name = '{}-{}'.format('ab', timestamp)
if not check_endpoint_config_existence(endpoint_config_name):
endpoint_config = sm.create_endpoint_config(
EndpointConfigName=endpoint_config_name,
ProductionVariants=[variantA, variantB]
)
pprint(endpoint_config)
else:
print("Endp... | {'EndpointConfigArn': 'arn:aws:sagemaker:us-east-1:299076282420:endpoint-config/ab-1638384450',
'ResponseMetadata': {'HTTPHeaders': {'content-length': '94',
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 01 Dec 2021 18:51:17 GMT',... | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Construct the endpoint name. | model_ab_endpoint_name = '{}-{}'.format('ab', timestamp)
print('Endpoint name: {}'.format(model_ab_endpoint_name)) | Endpoint name: ab-1638384450
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Exercise 6Create an endpoint with the endpoint name and configuration defined above. | if not check_endpoint_existence(model_ab_endpoint_name):
endpoint_response = sm.create_endpoint(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
EndpointName=model_ab_endpoint_name, # Replace None
EndpointConfigName=endpoint_config_name # Replace None
### END SOL... | Creating endpoint ab-1638384450
{'EndpointArn': 'arn:aws:sagemaker:us-east-1:299076282420:endpoint/ab-1638384450',
'ResponseMetadata': {'HTTPHeaders': {'content-length': '81',
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 01 Dec ... | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Review the created endpoint configuration in the AWS console.**Instructions**:- open the link- notice that you are in the section Amazon SageMaker -> Endpoint configuration- check the name of the endpoint configuration, its Amazon Resource Name (ARN) and production variants- click on the production variants and check t... | from IPython.core.display import display, HTML
display(
HTML(
'<b>Review <a target="blank" href="https://console.aws.amazon.com/sagemaker/home?region={}#/endpointConfig/{}">REST Endpoint configuration</a></b>'.format(
region, endpoint_config_name
)
)
) | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Review the created endpoint in the AWS console.**Instructions**:- open the link- notice that you are in the section Amazon SageMaker -> Endpoints- check the name of the endpoint, its ARN and status- below you can review the monitoring metrics such as CPU, memory and disk utilization. Further down you can see the endpoi... | from IPython.core.display import display, HTML
display(HTML('<b>Review <a target="blank" href="https://console.aws.amazon.com/sagemaker/home?region={}#/endpoints/{}">SageMaker REST endpoint</a></b>'.format(region, model_ab_endpoint_name))) | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Wait for the endpoint to deploy. _This cell will take approximately 5-10 minutes to run._ | %%time
waiter = sm.get_waiter('endpoint_in_service')
waiter.wait(EndpointName=model_ab_endpoint_name) | CPU times: user 220 ms, sys: 18.9 ms, total: 238 ms
Wall time: 8min 32s
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
_Wait until the ^^ endpoint ^^ is deployed_ 2. Test model 2.1. Test the model on a few sample stringsHere, you will pass sample strings of text to the endpoint in order to see the sentiment. You are given one example of each, however, feel free to play around and change the strings yourself! Exercise 7Create an Amaz... | from sagemaker.predictor import Predictor
from sagemaker.serializers import JSONLinesSerializer
from sagemaker.deserializers import JSONLinesDeserializer
inputs = [
{"features": ["I love this product!"]},
{"features": ["OK, but not great."]},
{"features": ["This is not the right product."]},
]
predictor =... | Predicted class 1 with probability 0.9605445861816406
Predicted class 0 with probability 0.5798221230506897
Predicted class -1 with probability 0.7667604684829712
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
2.2. Generate traffic and review the endpoint performance metricsNow you will generate traffic. To analyze the endpoint performance you will review some of the metrics that Amazon SageMaker emits in CloudWatch: CPU Utilization, Latency and Invocations. Full list of namespaces and metrics can be found [here](https://do... | def plot_endpoint_metrics_for_variants(endpoint_name,
namespace_name,
metric_name,
variant_names,
start_time,
end_time):... | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Establish wide enough time bounds to show all the charts using the same timeframe: | from datetime import datetime, timedelta
start_time = datetime.now() - timedelta(minutes=30)
end_time = datetime.now() + timedelta(minutes=30)
print('Start Time: {}'.format(start_time))
print('End Time: {}'.format(end_time)) | Start Time: 2021-12-01 18:38:05.978052
End Time: 2021-12-01 19:38:05.978095
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Set the list of the the variant names to analyze. | variant_names = [variantA["VariantName"], variantB["VariantName"]]
print(variant_names) | ['VariantA', 'VariantB']
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Run some predictions and view the metrics for each variant. _This cell will take approximately 1-2 minutes to run._ | %%time
for i in range(0, 100):
predicted_classes = predictor.predict(inputs) | CPU times: user 231 ms, sys: 7.57 ms, total: 239 ms
Wall time: 1min 37s
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
_Μake sure the predictions ^^ above ^^ ran successfully_ Let’s query CloudWatch to get a few metrics that are split across variants. If you see `Metrics not yet available`, please be patient as metrics may take a few mins to appear in CloudWatch. | time.sleep(30) # Sleep to accomodate a slight delay in metrics gathering
# CPUUtilization
# The sum of each individual CPU core's utilization.
# The CPU utilization of each core can range between 0 and 100. For example, if there are four CPUs, CPUUtilization can range from 0% to 400%.
plot_endpoint_metrics_for_variant... | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
3. Shift the traffic to one variant and review the endpoint performance metricsGenerally, the winning model would need to be chosen. The decision would be made based on the endpoint performance metrics and some other business related evaluations. Here you can assume that the winning model is in the Variant B and shift... | updated_endpoint_config = [
{
"VariantName": variantA["VariantName"],
"DesiredWeight": 0,
},
{
"VariantName": variantB["VariantName"],
"DesiredWeight": 100,
},
] | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Exercise 8Update variant weights in the configuration of the existing endpoint.**Instructions**: Use the `sm.update_endpoint_weights_and_capacities` function, passing the endpoint name and list of updated weights for each of the variants that you defined above. | sm.update_endpoint_weights_and_capacities(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
EndpointName=model_ab_endpoint_name, # Replace None
DesiredWeightsAndCapacities=updated_endpoint_config # Replace None
### END SOLUTION - DO NOT delete this comment for grading purposes
) | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
_Wait for the ^^ endpoint update ^^ to complete above_This may take a few minutes. Please be patient. _There is **no downtime** while the update is applying._ While waiting for the update (or afterwards) you can review the endpoint in the AWS console.**Instructions**:- open the link- notice that you are in the section... | from IPython.core.display import display, HTML
display(HTML('<b>Review <a target="blank" href="https://console.aws.amazon.com/sagemaker/home?region={}#/endpoints/{}">SageMaker REST endpoint</a></b>'.format(region, model_ab_endpoint_name)))
waiter = sm.get_waiter("endpoint_in_service")
waiter.wait(EndpointName=model_ab... | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Run some more predictions and view the metrics for each variant. _This cell will take approximately 1-2 minutes to run._ | %%time
for i in range(0, 100):
predicted_classes = predictor.predict(inputs) | CPU times: user 222 ms, sys: 15.7 ms, total: 238 ms
Wall time: 1min 31s
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
_Μake sure the predictions ^^ above ^^ ran successfully_If you see `Metrics not yet available`, please be patient as metrics may take a few minutes to appear in CloudWatch. Compare the results with the plots above. | # CPUUtilization
# The sum of each individual CPU core's utilization.
# The CPU utilization of each core can range between 0 and 100. For example, if there are four CPUs, CPUUtilization can range from 0% to 400%.
plot_endpoint_metrics_for_variants(
endpoint_name=model_ab_endpoint_name,
namespace_name="/aws/sa... | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
4. Configure one variant to autoscaleLet's configure Variant B to autoscale. You would not autoscale Variant A since no traffic is being passed to it at this time.First, you need to define a scalable target. It is an AWS resource and in this case you want to scale a `sagemaker` resource as indicated in the `ServiceNam... | autoscale.register_scalable_target(
ServiceNamespace="sagemaker",
ResourceId="endpoint/" + model_ab_endpoint_name + "/variant/VariantB",
ScalableDimension="sagemaker:variant:DesiredInstanceCount",
MinCapacity=1,
MaxCapacity=2,
RoleARN=role,
SuspendedState={
"DynamicScalingInSuspended... | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Check that the parameters from the function above are in the description of the scalable target: | autoscale.describe_scalable_targets(
ServiceNamespace="sagemaker",
MaxResults=100,
) | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Define and apply scaling policy using the `put_scaling_policy` function. The scaling policy provides additional information about the scaling behavior for your instance. `TargetTrackingScaling` refers to a specific autoscaling type supported by SageMaker, that uses a scaling metric and a target value as the indicator t... | autoscale.put_scaling_policy(
PolicyName="bert-reviews-autoscale-policy",
ServiceNamespace="sagemaker",
ResourceId="endpoint/" + model_ab_endpoint_name + "/variant/VariantB",
ScalableDimension="sagemaker:variant:DesiredInstanceCount",
PolicyType="TargetTrackingScaling",
TargetTrackingScalingPoli... | _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Generate traffic again and review the endpoint in the AWS console. _This cell will take approximately 1-2 minutes to run._ | %%time
for i in range(0, 100):
predicted_classes = predictor.predict(inputs) | CPU times: user 215 ms, sys: 19.2 ms, total: 234 ms
Wall time: 1min 31s
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Review the autoscaling:- open the link- notice that you are in the section Amazon SageMaker -> Endpoints- below you can see the endpoint runtime settings with the instance counts. You can run the predictions multiple times to observe the increase of the instance count to 2 | from IPython.core.display import display, HTML
display(HTML('<b>Review <a target="blank" href="https://console.aws.amazon.com/sagemaker/home?region={}#/endpoints/{}">SageMaker REST endpoint</a></b>'.format(region, model_ab_endpoint_name)))
| _____no_output_____ | MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
Upload the notebook into S3 bucket for grading purposes.**Note:** you may need to click on "Save" button before the upload. | !aws s3 cp ./C3_W2_Assignment.ipynb s3://$bucket/C3_W2_Assignment_Learner.ipynb | upload: ./C3_W2_Assignment.ipynb to s3://sagemaker-us-east-1-299076282420/C3_W2_Assignment_Learner.ipynb
| MIT | Course-3-Optimize ML Models and Deploy Human-in-the-Loop Pipelines/Week-2/C3_W2_Assignment.ipynb | BhargavTumu/coursera-practical-data-science-specialization |
import numpy as np
import tensorflow as tf
from tensorflow import keras
import pandas as pd
import seaborn as sns
from pylab import rcParams
import matplotlib.pyplot as plt
from matplotlib import rc
from sklearn.model_selection import train_test_split
from pandas.plotting import register_matplotlib_converters
%matplot... | _____no_output_____ | CC0-1.0 | datascience/models/regression.ipynb | VimeshShahama/Cyber---SDGP | |
Class Coding Lab: Introduction to ProgrammingThe goals of this lab are to help you to understand:1. the Jupyter and IDLE programming environments1. basic Python Syntax2. variables and their use3. how to sequence instructions together into a cohesive program4. the input() function for input and print() function for out... | your_name = input("What is your name? ")
print('Hello there',your_name) | _____no_output_____ | MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
Believe it or not there's a lot going on in this simple two-line program, so let's break it down. - The first line: - Asks you for input, prompting you `What is your Name?` - It then stores your input in the variable `your_name` - The second line: - prints out the following text: `Hello there` - then prints out th... | # Here's an example of variable assignment. Wre
country = 'USA' | _____no_output_____ | MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
Variable Access What good is storing data if you cannot retrieve it? Lucky for us, retrieving the data in variable is as simple as calling its name: | country # This should say 'USA' | _____no_output_____ | MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
At this point you might be thinking: Can I overwrite a variable? The answer, of course, is yes! Just re-assign it a different value: | country = 'Canada' | _____no_output_____ | MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
You can also access a variable multiple times. Each time it simply gives you its value: | country, country, country | _____no_output_____ | MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
The Purpose Of VariablesVariables play an vital role in programming. Computer instructions have no memory of each other. That is one line of code has no idea what is happening in the other lines of code. The only way we can "connect" what happens from one line to the next is through variables. For example, if we re-wr... | input("What is your name? ")
print('Hello there') | What is your name? Bob
Hello there
| MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
When you execute this program, notice there is no longer a connection between the input and the output. In fact, the input on line 1 doesn't matter because the output on line 2 doesn't know about it. It cannot because we never stored the results of the input into a variable! What's in a name? Um, EVERYTHINGComputer co... | y = input("Enter your city: ")
x = input("Enter your state: ")
print(x,y,'is a nice place to live') | Enter your city: yeet town
Enter your state: yeet state
yeet state yeet town is a nice place to live
| MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
What do `x` and `y` represent? Is there a semantic (design) error in this program?You might find it easy to figure out the answers to these questions, but consider this more human-friendly version: | state = input("Enter your city: ")
city = input("Enter your state: ")
print(city,state,'is a nice place to live') | _____no_output_____ | MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
Do the aptly-named variables make it easier to find the semantic errors in this second version? You Do It:Finally re-write this program so that it uses well-thought out variables AND in semantically correct: | # TODO: Code it re-write the above program to work as it should: Stating City State is a nice place to live
city = input("Enter your city: ")
state = input("Enter your state: ")
print(city + ",", state, "is a nice place to live") | Enter your city: Yeet Town
Enter your state: Yeet State
Yeet Town, Yeet State is a nice place to live
| MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
Now Try This:Now try to write a program which asks for two separate inputs: your first name and your last name. The program should then output `Hello` with your first name and last name.For example if you enter `Mike` for the first name and `Fudge` for the last name the program should output `Hello Mike Fudge`**HINTS*... | # TODO: write your code here
first_name = input("What's your name? ")
last_name = input("What's your last name? ")
print ("Hello,",first_name,last_name) | What's your name? Bob
What's your last name? Bobson
Hello, Bob Bobson
| MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
Variable Concatenation: Your First OperatorThe `+` symbol is used to combine to variables containing text values together. Consider the following example: | prefix = "re"
suffix = "ment"
root = input("Enter a root word, like 'ship': ")
print( prefix + root + suffix) | Enter a root word, like 'ship': yeet
reyeetment
| MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
Now Try ThisWrite a program to prompt for three colors as input, then outputs those three colors as a lis, informing me which one was the middle (2nd entered) color. For example if you were to enter `red` then `green` then `blue` the program would output: `Your colors were: red, green, and blue. The middle was is gree... | # TODO: write your code here
first_color = input("Choose a color: ")
second_color = input("Choose another color: ")
third_color = input("Choose another color: ")
print("Your colors were", first_color + ",", second_color + ", and", third_color + ". The middle color was", second_color + ".")
| Choose a color: red
Choose another color: yellow
Choose another color: blue
Your colors were red, yellow, and blue. The middle color was yellow.
| MIT | content/lessons/02/Class-Coding-Lab/CCL-Intro-To-Python-Programming.ipynb | MahopacHS/spring2019-ditoccoa0302 |
Before your start:- Read the README.md file- Comment as much as you can and use the resources (README.md file)- Happy learning! | #import numpy and pandas
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
Challenge 1 - The `stats` SubmoduleThis submodule contains statistical functions for conducting hypothesis tests, producing various distributions and other useful tools. Let's examine this submodule using the KickStarter dataset. Load the data using Ironhack's database (db: kickstarter, table: projects). | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
Now print the `head` function to examine the dataset. | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
Import the `mode` function from `scipy.stats` and find the mode of the `country` and `currency` column. | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
The trimmed mean is a function that computes the mean of the data with observations removed. The most common way to compute a trimmed mean is by specifying a percentage and then removing elements from both ends. However, we can also specify a threshold on both ends. The goal of this function is to create a more robust ... | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
SciPy contains various statistical tests. One of the tests is Fisher's exact test. This test is used for contingency tables. The test originates from the "Lady Tasting Tea" experiment. In 1935, Fisher published the results of the experiment in his book. The experiment was based on a claim by Muriel Bristol that she ca... | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
Next, create a column that checks whether the currency of the project is in US Dollars. Create a column called `usd` using the `np.where` function where if the currency is US Dollars, assign a value of 1 to the row and 0 otherwise. | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
Now create a contingency table using the `pd.crosstab` function in the cell below to compare the `goal_met` and `usd` columns. Import the `fisher_exact` function from `scipy.stats` and conduct the hypothesis test on the contingency table that you have generated above. You can read more about the `fisher_exact` function... | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
Challenge 2 - The `interpolate` submoduleThis submodule allows us to interpolate between two points and create a continuous distribution based on the observed data.In the cell below, import the `interp1d` function and first take a sample of 10 rows from `kickstarter`. | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
Next, create a linear interpolation of the backers as a function of `usd_pledged_real`. Create a function `f` that generates a linear interpolation of backers as predicted by the amount of real pledged dollars. | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
Now create a new variable called `x_new`. This variable will contain all integers between the minimum number of backers in our sample and the maximum number of backers. The goal here is to take the dataset that contains few obeservations due to sampling and fill all observations with a value using the interpolation fun... | # Your code here:
| _____no_output_____ | MIT | module-2/Intro-Scipy/your-code/main.ipynb | Skye-FY/daft-miami-0120-labs |
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