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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Add the Number of Rows to be considered as training data, test data and validation data in the input CSV which contains the training data | Trainrow = 1
testrow = 45
valrow = 49 | _____no_output_____ | MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
Reading the input text from CSV file mentioned here. Change according to requirement. | import json
def format_to_lines():
p_ct = 0
i = 0
for i in range(Trainrow):
df_csv = pd.read_csv('./fincident.csv',encoding='cp1252')
dataset = []
d = _format_to_lines_new(df_csv,i)
dataset.append(d)
i += 1
pt_file = "{:s}.{:s}.{:d}.json".format('bert_data', "... | _____no_output_____ | MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
Genereate JSON file of the trianing data from th CSV | format_to_lines() | Hello Colleagues The solution creation is failing in QTC 906 with all the users with generic error message saying authorisation is missing. but with the same users the solutions were created previously also. NOTE : Even after the error message is shown the solution can be seen in solution explorer window but when clic... | MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
Different Modules which are required below for Preprocessing | def _get_word_ngrams(n, sentences):
"""Calculates word n-grams for multiple sentences.
"""
assert len(sentences) > 0
assert n > 0
# words = _split_into_words(sentences)
words = sum(sentences, [])
# words = [w for w in words if w not in stopwords]
return _get_ngrams(n, words)
def _get_n... | _____no_output_____ | MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
Execute the below code for Preprocessing for PreSumm Only | # For PreSumm
class BertData():
def __init__(self):
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
self.sep_token = '[SEP]'
self.cls_token = '[CLS]'
self.pad_token = '[PAD]'
self.tgt_bos = '[unused0]'
self.tgt_eos = '[unused1... | _____no_output_____ | MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
Execute the below code for Preprocessing for BertSumm Only | # For BertSum
class BertData():
def __init__(self):
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
self.sep_vid = self.tokenizer.vocab['[SEP]']
self.cls_vid = self.tokenizer.vocab['[CLS]']
self.pad_vid = self.tokenizer.vocab['[PAD]']
def ... | _____no_output_____ | MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
Execute the below code for Preprocessing for PreSumm Only | ## For PreSumm Only
def _format_to_bert():
p_ct = 0
for i in range(Trainrow):
pt_file = "{:s}.{:s}.{:d}.json".format('bert_data', "train", p_ct)
json_file = pt_file
save_file = pt_file.replace('.json','.pt')
p_ct += 1
bert = BertData()
jobs = json.load(open(json_file))
datasets = []
... | _____no_output_____ | MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
Execute the below code for Preprocessing for PreSumm Only | ## For BertSumm Only
def _format_to_bert():
p_ct = 0
for i in range(Trainrow):
pt_file = "{:s}.{:s}.{:d}.json".format('bert_data', "train", p_ct)
json_file = pt_file
save_file = pt_file.replace('.json','.pt')
p_ct += 1
bert = BertData()
jobs = json.load(open(json_file))
datasets = []
... | inside
[0, 18, 22]
{'src': [101, 7592, 8628, 1996, 5576, 4325, 2003, 7989, 1999, 1053, 13535, 3938, 2575, 2007, 2035, 1996, 5198, 2007, 12391, 7561, 4471, 3038, 3166, 6648, 2003, 4394, 1012, 102, 101, 2021, 2007, 1996, 2168, 5198, 1996, 7300, 2020, 2580, 3130, 2036, 1012, 102, 101, 3602, 1024, 2130, 2044, 1996, 7561, 4... | MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
Image analysis in Python with SciPy and scikit-imageTo participate, please follow the preparation instructions athttps://github.com/scikit-image/skimage-tutorials/(click on **preparation.md**).TL;DR: Install Python 3.6, scikit-image, and the Jupyter notebook. Then clone this repo:```pythongit clone --depth=1 https://... | %run ../../check_setup.py | [✓] scikit-image 0.15.0
[✓] numpy 1.16.2
[✓] scipy 1.2.1
[✓] matplotlib 3.0.3
[✓] notebook 5.7.6
[✓] scikit-learn 0.20.3
| CC-BY-4.0 | workshops/2019-scipy/index.ipynb | whocaresustc/skimage-tutorials |
Tf-idf Walkthrough for scikit-learn When I was using scikit-learn extremely handy `TfidfVectorizer` I had some trouble interpreting the results since they seem to be a little bit different from the standard convention of how the *term frequency-inverse document frequency* (tf-idf) is calculated. Here, I just put toget... | import numpy as np
docs = np.array([
'The sun is shining',
'The weather is sweet',
'The sun is shining and the weather is sweet']) | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
Raw term frequency [[back to top](Sections)] First, we will start with the *raw term frequency* tf(t, d), which is defined by the number of times a term *t* occurs in a document *t*Alternative term frequency definitions include the binary term frequency, log-scaled term frequency, and augmented term frequency [[1](R... | from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer()
tf = cv.fit_transform(docs).toarray()
tf
cv.vocabulary_ | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
Based on the vocabulary, the word "and" would be the 1st feature in each document vector in `tf`, the word "is" the 2nd, the word "shining" the 3rd, etc. Normalized term frequency [[back to top](Sections)] In this section, we will take a brief look at how the tf-feature vector can be normalized, which will be useful ... | tf_norm = tf[2] / np.sqrt(np.sum(tf[2]**2))
tf_norm | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
In scikit-learn, we can use the [`TfidfTransformer`](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html) to normalize the term frequencies if we disable the inverse document frequency calculation (`use_idf: False` and `smooth_idf=False`): | from sklearn.feature_extraction.text import TfidfTransformer
tfidf = TfidfTransformer(use_idf=False, norm='l2', smooth_idf=False)
tf_norm = tfidf.fit_transform(tf).toarray()
print('Normalized term frequencies of document 3:\n %s' % tf_norm[-1]) | Normalized term frequencies of document 3:
[ 0.2773501 0.5547002 0.2773501 0.2773501 0.2773501 0.5547002
0.2773501]
| MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
Term frequency-inverse document frequency -- tf-idf [[back to top](Sections)] Most commonly, the term frequency-inverse document frequency (tf-idf) is calculated as follows [[1](References)]: $$\text{tf-idf}(t, d) = \text{tf}(t, d) \times \text{idf}(t),$$where idf is the inverse document frequency, which we will intr... | n_docs = len(docs)
df_and = 1
idf_and = np.log(n_docs / (1 + df_and))
print('idf "and": %s' % idf_and)
df_is = 3
idf_is = np.log(n_docs / (1 + df_is))
print('idf "is": %s' % idf_is)
df_shining = 2
idf_shining = np.log(n_docs / (1 + df_shining))
print('idf "shining": %s' % idf_shining) | idf "and": 0.405465108108
idf "is": -0.287682072452
idf "shining": 0.0
| MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
Using those idfs, we can eventually calculate the tf-idfs for the 3rd document: | print('Tf-idfs in document 3:\n')
print('tf-idf "and": %s' % (1 * idf_and))
print('tf-idf "is": %s' % (2 * idf_is))
print('tf-idf "shining": %s' % (1 * idf_shining)) | Tf-idfs in document 3:
tf-idf "and": 0.405465108108
tf-idf "is": -0.575364144904
tf-idf "shining": 0.0
| MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
Tf-idf in scikit-learn [[back to top](Sections)] The tf-idfs in scikit-learn are calculated a little bit differently. Here, the `+1` count is added to the idf, whereas instead of the denominator if the df: $$\text{idf}(t) = log{\frac{n_d}{\text{df}(d,t)}} + 1$$ We can demonstrate this by calculating the idfs manuall... | tf_and = 1
df_and = 1
tf_and * (np.log(n_docs / df_and) + 1)
tf_shining = 2
df_shining = 3
tf_shining * (np.log(n_docs / df_shining) + 1)
tf_is = 1
df_is = 2
tf_is * (np.log(n_docs / df_is) + 1)
tfidf = TfidfTransformer(use_idf=True, smooth_idf=False, norm=None)
tfidf.fit_transform(tf).toarray()[-1][0:3] | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
Normalized tf-idf [[back to top](Sections)] Now, let us calculate the normalized tf-idfs. Our feature vector of un-normalized tf-idfs for document 3 would look as follows if we'd applied the equation from the previous section to all words in the document: | tf_idfs_d3 = np.array([[ 2.09861229, 2.0, 1.40546511, 1.40546511, 1.40546511, 2.0, 1.40546511]]) | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
Using the l2-norm, we then normalize the tf-idfs as follows: | tf_idfs_d3_norm = tf_idfs_d3[-1] / np.sqrt(np.sum(tf_idfs_d3[-1]**2))
tf_idfs_d3_norm | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
And finally, we compare the results to the results that the `TfidfTransformer` returns. | tfidf = TfidfTransformer(use_idf=True, smooth_idf=False, norm='l2')
tfidf.fit_transform(tf).toarray()[-1] | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
Smooth idf [[back to top](Sections)] Another parameter in the `TfidfTransformer` is the `smooth_idf`, which is described as> smooth_idf : boolean, default=True Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zer... | tfidf = TfidfTransformer(use_idf=True, smooth_idf=True, norm=None)
tfidf.fit_transform(tf).toarray()[-1][:3]
tf_and = 1
df_and = 1
tf_and * (np.log((n_docs+1) / (df_and+1)) + 1)
tf_is = 2
df_is = 3
tf_is * (np.log((n_docs+1) / (df_is+1)) + 1)
tf_shining = 1
df_shining = 2
tf_shining * (np.log((n_docs+1) / (df_shi... | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
TfidfVectorizer defaults [[back to top](Sections)] Finally, we now understand the default settings in the `TfidfTransformer`, which are: - `use_idf=True`- `smooth_idf=True`- `norm='l2'` And the equation can be summarized as $\text{tf-idf} = \text{tf}(t) \times (\text{idf}(t, d) + 1),$ where $\text{idf}(t) = log{... | tfidf = TfidfTransformer()
tfidf.fit_transform(tf).toarray()[-1]
smooth_tfidfs_d3 = np.array([[ 1.69314718, 2.0, 1.28768207, 1.28768207, 1.28768207, 2.0, 1.28768207]])
smooth_tfidfs_d3_norm = smooth_tfidfs_d3[-1] / np.sqrt(np.sum(smooth_tfidfs_d3[-1]**2))
smooth_tfidfs_d3_norm | _____no_output_____ | MIT | pattern-classification/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb | gopala-kr/ds-notebooks |
This notebook is part of the $\omega radlib$ documentation: http://wradlib.org/wradlib-docs.Copyright (c) 2018, $\omega radlib$ developers.Distributed under the MIT License. See LICENSE.txt for more info. Beam Blockage Calculation using a DEM Here, we derive (**p**artial) **b**eam-**b**lockage (**PBB**) from a **D**ig... | import wradlib as wrl
import matplotlib.pyplot as pl
import matplotlib as mpl
import warnings
warnings.filterwarnings('ignore')
try:
get_ipython().magic("matplotlib inline")
except:
pl.ion()
import numpy as np | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
Setup for Bonn radar First, we need to define some radar specifications (here: *University of Bonn*). | sitecoords = (7.071663, 50.73052, 99.5)
nrays = 360 # number of rays
nbins = 1000 # number of range bins
el = 1.0 # vertical antenna pointing angle (deg)
bw = 1.0 # half power beam width (deg)
range_res = 100. # range resolution (meters) | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
Create the range, azimuth, and beamradius arrays. | r = np.arange(nbins) * range_res
beamradius = wrl.util.half_power_radius(r, bw) | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
We use - [wradlib.georef.sweep_centroids](http://wradlib.org/wradlib-docs/latest/generated/wradlib.georef.sweep_centroids.html) and - [wradlib.georef.spherical_to_proj](http://wradlib.org/wradlib-docs/latest/generated/wradlib.georef.spherical_to_proj.html) to calculate the spherical coordinates of the bin centroids an... | coord = wrl.georef.sweep_centroids(nrays, range_res, nbins, el)
coords = wrl.georef.spherical_to_proj(coord[..., 0],
np.degrees(coord[..., 1]),
coord[..., 2], sitecoords)
lon = coords[..., 0]
lat = coords[..., 1]
alt = coords[..., 2]
polcoords... | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
Preprocessing the digitial elevation model - Read the DEM from a ``geotiff`` file (in `WRADLIB_DATA`);- clip the region inside the bounding box;- map the DEM values to the polar grid points. *Note*: You can choose between the coarser resolution `bonn_gtopo.tif` (from GTOPO30) and the finer resolution `bonn_new.tif` (f... | #rasterfile = wrl.util.get_wradlib_data_file('geo/bonn_gtopo.tif')
rasterfile = wrl.util.get_wradlib_data_file('geo/bonn_new.tif')
ds = wrl.io.open_raster(rasterfile)
rastervalues, rastercoords, proj = wrl.georef.extract_raster_dataset(ds, nodata=-32768.)
# Clip the region inside our bounding box
ind = wrl.util.find... | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
Calculate Beam-Blockage Now we can finally apply the [wradlib.qual.beam_block_frac](http://wradlib.org/wradlib-docs/latest/generated/wradlib.qual.beam_block_frac.html) function to calculate the PBB. | PBB = wrl.qual.beam_block_frac(polarvalues, alt, beamradius)
PBB = np.ma.masked_invalid(PBB)
print(PBB.shape) | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
So far, we calculated the fraction of beam blockage for each bin.But we need to into account that the radar signal travels along a beam. Cumulative beam blockage (CBB) in one bin along a beam will always be at least as high as the maximum PBB of the preceeding bins (see [wradlib.qual.cum_beam_block_frac](http://wradlib... | CBB = wrl.qual.cum_beam_block_frac(PBB)
print(CBB.shape) | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
Visualize Beamblockage Now we visualize- the average terrain altitude per radar bin- a beam blockage map- interaction with terrain along a single beam | # just a little helper function to style x and y axes of our maps
def annotate_map(ax, cm=None, title=""):
ticks = (ax.get_xticks()/1000).astype(np.int)
ax.set_xticklabels(ticks)
ticks = (ax.get_yticks()/1000).astype(np.int)
ax.set_yticklabels(ticks)
ax.set_xlabel("Kilometers")
ax.set_ylabel("Ki... | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
Visualize Beam Propagation showing earth curvature Now we visualize- interaction with terrain along a single beamIn this representation the earth curvature is shown. For this we assume the earth a sphere with exactly 6370000 m radius. This is needed to get the height ticks at nice position. | def height_formatter(x, pos):
x = (x - 6370000) / 1000
fmt_str = '{:g}'.format(x)
return fmt_str
def range_formatter(x, pos):
x = x / 1000.
fmt_str = '{:g}'.format(x)
return fmt_str | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
- The [wradlib.vis.create_cg](http://wradlib.org/wradlib-docs/latest/generated/wradlib.vis.create_cg.html)-function is facilitated to create the curved geometries. - The actual data is plottet as (theta, range) on the parasite axis. - Some tweaking is needed to get the final plot look nice. | fig = pl.figure(figsize=(10, 6))
cgax, caax, paax = wrl.vis.create_cg('RHI', fig, 111)
# azimuth angle
angle = 225
# fix grid_helper
er = 6370000
gh = cgax.get_grid_helper()
gh.grid_finder.grid_locator2._nbins=80
gh.grid_finder.grid_locator2._steps=[1,2,4,5,10]
# calculate beam_height and arc_distance for ke=1
# me... | _____no_output_____ | MIT | notebooks/beamblockage/wradlib_beamblock.ipynb | kmuehlbauer/wradlib-notebooks |
Deploy SageMaker Serverless Endpoint Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)- KoELECTRA: https://github.com/monologg/KoELECTRA- Naver Sentiment Movie Corpus Dataset: https://github.com/e9t/nsmc--- OverviewAmazon SageMaker Serverless Inference... | !pip install -qU sagemaker botocore boto3 awscli
!pip install --ignore-installed PyYAML
!pip install transformers==4.12.5
import torch
import torchvision
import torchvision.models as models
import sagemaker
from sagemaker import get_execution_role
from sagemaker.utils import name_from_base
from sagemaker.pytorch import... | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
2. Create SageMaker Serverless Endpoint---SageMaker Serverless Endpoint는 기존 SageMaker 리얼타임 엔드포인트 배포와 99% 유사합니다. 1%의 차이가 무엇일까요? Endpoint 구성 설정 시, ServerlessConfig에서 메모리 크기(`MemorySizeInMB`), 최대 동시 접속(`MaxConcurrency`)에 대한 파라메터만 추가하시면 됩니다.```pythonsm_client.create_endpoint_config( ... "ServerlessConfig": { "MemoryS... | from sagemaker.image_uris import retrieve
deploy_instance_type = 'ml.m5.xlarge'
pt_ecr_image_uri = retrieve(
framework='pytorch',
region=region,
version='1.7.1',
py_version='py3',
instance_type = deploy_instance_type,
accelerator_type=None,
image_scope='inference'
) | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
Create a SageMaker Model`create_model` API를 호출하여 위 코드 셀에서 생성한 컨테이너의 정의를 포함하는 모델을 생성합니다. | model_name = f"KorNLPServerless-{nlp_task}-{strftime('%Y-%m-%d-%H-%M-%S', gmtime())}"
create_model_response = sm_client.create_model(
ModelName=model_name,
Containers=[
{
"Image": pt_ecr_image_uri,
"Mode": "SingleModel",
"ModelDataUrl": model_s3_uri,
"Env... | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
Create Endpoint Configuration`ServerlessConfig`으로 엔드포인트에 대한 서버리스 설정을 조정할 수 있습니다. 최대 동시 호출(`MaxConcurrency`; max concurrent invocations)은 1에서 50 사이이며, `MemorySize`는 1024MB, 2048MB, 3072MB, 4096MB, 5120MB 또는 6144MB를 선택할 수 있습니다. | endpoint_config_name = f"KorNLPServerlessEndpointConfig-{nlp_task}-{strftime('%Y-%m-%d-%H-%M-%S', gmtime())}"
endpoint_config_response = sm_client.create_endpoint_config(
EndpointConfigName=endpoint_config_name,
ProductionVariants=[
{
"VariantName": "AllTraffic",
"ModelName": mod... | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
Create a SageMaker Multi-container endpointcreate_endpoint API로 멀티 컨테이너 엔드포인트를 생성합니다. 기존의 엔드포인트 생성 방법과 동일합니다. | endpoint_name = f"KorNLPServerlessEndpoint-{nlp_task}-{strftime('%Y-%m-%d-%H-%M-%S', gmtime())}"
endpoint_response = sm_client.create_endpoint(
EndpointName=endpoint_name,
EndpointConfigName=endpoint_config_name
)
print(f"Creating Endpoint: {endpoint_response['EndpointArn']}") | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
`describe_endpoint` API를 사용하여 엔드포인트 생성 상태를 확인할 수 있습니다. SageMaker 서버리스 엔드포인트는 일반적인 엔드포인트 생성보다 빠르게 생성됩니다. (약 2-3분) | %%time
waiter = boto3.client('sagemaker').get_waiter('endpoint_in_service')
print("Waiting for endpoint to create...")
waiter.wait(EndpointName=endpoint_name)
resp = sm_client.describe_endpoint(EndpointName=endpoint_name)
print(f"Endpoint Status: {resp['EndpointStatus']}") | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
Direct Invocation for Model 최초 호출 시 Cold start로 지연 시간이 발생하지만, 최초 호출 이후에는 warm 상태를 유지하기 때문에 빠르게 응답합니다. 물론 수 분 동안 호출이 되지 않거나 요청이 많아지면 cold 상태로 바뀐다는 점을 유의해 주세요. | model_sample_path = 'samples/nsmc.txt'
!cat $model_sample_path
with open(model_sample_path, mode='rb') as file:
model_input_data = file.read()
model_response = sm_runtime.invoke_endpoint(
EndpointName=endpoint_name,
ContentType="application/jsonlines",
Accept="application/jsonlines",
Body=model_i... | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
Check Model Latency | import time
start = time.time()
for _ in range(10):
model_response = sm_runtime.invoke_endpoint(
EndpointName=endpoint_name,
ContentType="application/jsonlines",
Accept="application/jsonlines",
Body=model_input_data
)
inference_time = (time.time()-start)
print(f'Inference time is {inference_time:.4f... | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
Clean Up--- | sm_client.delete_endpoint(EndpointName=endpoint_name)
sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)
sm_client.delete_model(ModelName=model_name) | _____no_output_____ | MIT | key_features/ptn_4.2_serverless-inference/serverless_endpoint_kornlp_nsmc.ipynb | aws-samples/sm-model-serving-patterns |
Datenvalidierung mit Voluptuous (Schemadefinitionen)In diesem Notebook verwenden wir [Voluptuous](https://github.com/alecthomas/voluptuous), um Schemata für unsere Daten zu definieren. Wir können dann die Schemaprüfung an verschiedenen Stellen unserer Bereinigung verwenden, um sicherzustellen, dass wir die Kriterien e... | import logging
import pandas as pd
from datetime import datetime
from voluptuous import Schema, Required, Range, All, ALLOW_EXTRA
from voluptuous.error import MultipleInvalid, Invalid | _____no_output_____ | BSD-3-Clause | docs/clean-prep/voluptuous.ipynb | veit/jupyter-tutorial-de |
2. Logger | logger = logging.getLogger(0)
logger.setLevel(logging.WARNING) | _____no_output_____ | BSD-3-Clause | docs/clean-prep/voluptuous.ipynb | veit/jupyter-tutorial-de |
3. Beispieldaten lesen | sales = pd.read_csv('https://raw.githubusercontent.com/kjam/data-cleaning-101/master/data/sales_data.csv') | _____no_output_____ | BSD-3-Clause | docs/clean-prep/voluptuous.ipynb | veit/jupyter-tutorial-de |
4. Daten untersuchen | sales.head()
sales.dtypes | _____no_output_____ | BSD-3-Clause | docs/clean-prep/voluptuous.ipynb | veit/jupyter-tutorial-de |
5. Schema definieren | schema = Schema({
Required('sale_amount'): All(float,
Range(min=2.50, max=1450.99)),
}, extra=ALLOW_EXTRA)
error_count = 0
for s_id, sale in sales.T.to_dict().items():
try:
schema(sale)
except MultipleInvalid as e:
logging.warning('issue with sale: %s (%s) -... | _____no_output_____ | BSD-3-Clause | docs/clean-prep/voluptuous.ipynb | veit/jupyter-tutorial-de |
Aktuell wissen wir jedoch noch nicht, ob* wir ein falsch definiertes Schema haben* möglicherweise negative Werte zurückgegeben oder falsch markiert werden* höhere Werte kombinierte Einkäufe oder Sonderverkäufe sind 6. Hinzufügen einer benutzerdefinierten Validierung | def ValidDate(fmt='%Y-%m-%d %H:%M:%S'):
return lambda v: datetime.strptime(v, fmt)
schema = Schema({
Required('timestamp'): All(ValidDate()),
}, extra=ALLOW_EXTRA)
error_count = 0
for s_id, sale in sales.T.to_dict().items():
try:
schema(sale)
except MultipleInvalid as e:
logging.warning(... | _____no_output_____ | BSD-3-Clause | docs/clean-prep/voluptuous.ipynb | veit/jupyter-tutorial-de |
7. Gültige Datumsstrukturen sind noch keine gültigen Daten | def ValidDate(fmt='%Y-%m-%d %H:%M:%S'):
def validation_func(v):
try:
assert datetime.strptime(v, fmt) <= datetime.now()
except AssertionError:
raise Invalid('date is in the future! %s' % v)
return validation_func
schema = Schema({
Required('timestamp'): All(ValidDate(... | _____no_output_____ | BSD-3-Clause | docs/clean-prep/voluptuous.ipynb | veit/jupyter-tutorial-de |
open boundary spin 1/2 1-D Heisenberg model$H = J \sum_i [S_i^z S_{i+1}^z + \frac{1}{2}(S_i^+ S_{i+1}^- + S_i^- S_{i+1}^+)]$exact result (Bethe Anstatz):L E/J16 -6.911737145574924 -10.453785760409632 -13.997315618224348 -21.085956314386364 -28.1754248597421 | from renormalizer.mps import Mps, Mpo, solver
from renormalizer.model import MolList2, ModelTranslator
from renormalizer.utils import basis as ba
from renormalizer.utils import Op
import numpy as np
# define the # of spins
nspin = 16
# define the model
# sigma^+ = S^+
# sigma^- = S^-
# 1/2 sigma^x,y,z = S^x,y,z
mode... | _____no_output_____ | Apache-2.0 | example/1D-Heisenberg.ipynb | liwt31/Renormalizer |
Striplog from CSV | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import striplog
striplog.__version__
from striplog import Lexicon, Decor, Component, Legend, Interval, Striplog | _____no_output_____ | Apache-2.0 | striplog_def_Matt.ipynb | ThomasMGeo/CSV2Strip |
Make legendMost of the stuff in the dicts you made were about **display** — so they are going to make `Decor` objects. A collection of `Decor`s makes a `Legend`. A `Legend` determines how a striplog is displayed.First I'll make the components, since those are easy. I'll move `'train'` into there too, since it is to do... | facies = {
's': Component({'lithology': 'sandstone', 'train':'y'}),
'i': Component({'lithology': 'interbedded', 'train':'y'}),
'sh': Component({'lithology': 'shale', 'train':'y'}),
'bs': Component({'lithology': 'sandstone', 'train': 'n'}),
't': Component({'lithology': 'turbidite', 'train':'y'}),
... | _____no_output_____ | Apache-2.0 | striplog_def_Matt.ipynb | ThomasMGeo/CSV2Strip |
Read CSV into striplog | strip = Striplog.from_csv('test.csv')
strip[0] | _____no_output_____ | Apache-2.0 | striplog_def_Matt.ipynb | ThomasMGeo/CSV2Strip |
Deal with lithologyThe lithology has been turned into a component, but it's using the abbreviation... I can't figure out an elegant way to deal with this so, for now, we'll just loop over the striplog and fix it. We read the `data` item's lithology (`'s'` in the top layer), then look up the correct lithology name in o... | for s in strip:
lith = s.data['lithology']
s.components = [facies[lith]]
s.data = {}
strip[0] | _____no_output_____ | Apache-2.0 | striplog_def_Matt.ipynb | ThomasMGeo/CSV2Strip |
That's better! | strip.plot(legend) | _____no_output_____ | Apache-2.0 | striplog_def_Matt.ipynb | ThomasMGeo/CSV2Strip |
Remove non-training layers | strip
strip_train = Striplog([s for s in strip if s.primary['train'] == 'y'])
strip_train
strip_train.plot(legend) | _____no_output_____ | Apache-2.0 | striplog_def_Matt.ipynb | ThomasMGeo/CSV2Strip |
This notebook aims to produce Figure S2 in Supplementary Material for the preprint: | import os
import random
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
import datetime
import time
import csv
import math
import scipy
import seaborn as sns
from scipy.stats import iqr
import h5py
import pickle
from tqdm import tqdm
import copy
from sklearn.ensemble import RandomFo... | _____no_output_____ | Apache-2.0 | ROC_classification.ipynb | yuewu57/mental_health_AMoSS |
Load data | test_path='./all-true-colours-matlab-2017-02-27-18-38-12-nick/'
participants_list, participants_data_list,\
participants_time_list=loadParticipants(test_path)
Participants=make_classes(participants_data_list,\
participants_time_list,\
participant... | 14050
| Apache-2.0 | ROC_classification.ipynb | yuewu57/mental_health_AMoSS |
**Length=20w** * Missing-response-incorporated signature-based classification model (MRSCM, level2) | y_tests_mc, y_scores_mc=model_roc(cohort, minlen=20, training=0.7,order=2, sample_size=100) )
plot_roc(y_tests_mc, y_scores_mc, "signature_method_missingcount_2ndtrial", n_classes=3,lw=2) | The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
| Apache-2.0 | ROC_classification.ipynb | yuewu57/mental_health_AMoSS |
* Missing-response-incorporated signature-based classification model (MRSCM, level3) | y_tests_mc3, y_scores_mc3=model_roc(cohort, minlen=20, training=0.7,order=3, sample_size=100)
plot_roc(y_tests_mc3, y_scores_mc3, "signature_method_missingcount3", n_classes=3,lw=2) | The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
| Apache-2.0 | ROC_classification.ipynb | yuewu57/mental_health_AMoSS |
* Naive classification model | if __name__ == "__main__":
y_tests_naive, y_scores_naive=model_roc(cohort, minlen=20,\
training=0.7,\
order=None,\
sample_size=100,\
... | The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
| Apache-2.0 | ROC_classification.ipynb | yuewu57/mental_health_AMoSS |
Copyright 2018 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
はじめてのニューラルネットワーク：分類問題の初歩 View on TensorFlow.org Run in Google Colab View source on GitHub Note: これらのドキュメントは私たちTensorFlowコミュニティが翻訳したものです。コミュニティによる 翻訳は**ベストエフォート**であるため、この翻訳が正確であることや[英語の公式ドキュメント](https://www.tensorflow.org/?hl=en)の 最新の状態を反映したものであることを保証することはできません。 この翻訳の品質を向上させるためのご意見をお持ちの方は、GitHubリポ... | try:
# Colab only
%tensorflow_version 2.x
except Exception:
pass
from __future__ import absolute_import, division, print_function, unicode_literals
# TensorFlow と tf.keras のインポート
import tensorflow as tf
from tensorflow import keras
# ヘルパーライブラリのインポート
import numpy as np
import matplotlib.pyplot as plt
print(t... | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
ファッションMNISTデータセットのロード このガイドでは、[Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist)を使用します。Fashion MNISTには10カテゴリーの白黒画像70,000枚が含まれています。それぞれは下図のような1枚に付き1種類の衣料品が写っている低解像度（28×28ピクセル）の画像です。 <img src="https://tensorflow.org/images/fashion-mnist-sprite.png" alt="Fashion MNIST sprite" width="600"> ... | fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
ロードしたデータセットは、NumPy配列になります。* `train_images` と `train_labels` の2つの配列は、モデルの訓練に使用される**訓練用データセット**です。* 訓練されたモデルは、 `test_images` と `test_labels` 配列からなる**テスト用データセット**を使ってテストします。画像は28×28のNumPy配列から構成されています。それぞれのピクセルの値は0から255の間の整数です。**ラベル**（label）は、0から9までの整数の配列です。それぞれの数字が下表のように、衣料品の**クラス**（class）に対応しています。 Label Class ... | class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
データの観察モデルの訓練を行う前に、データセットのフォーマットを見てみましょう。下記のように、訓練用データセットには28×28ピクセルの画像が60,000枚含まれています。 | train_images.shape | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
同様に、訓練用データセットには60,000個のラベルが含まれます。 | len(train_labels) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
ラベルはそれぞれ、0から9までの間の整数です。 | train_labels | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
テスト用データセットには、10,000枚の画像が含まれます。画像は28×28ピクセルで構成されています。 | test_images.shape | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
テスト用データセットには10,000個のラベルが含まれます。 | len(test_labels) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
データの前処理ネットワークを訓練する前に、データを前処理する必要があります。最初の画像を調べてみればわかるように、ピクセルの値は0から255の間の数値です。 | plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show() | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
ニューラルネットワークにデータを投入する前に、これらの値を0から1までの範囲にスケールします。そのためには、画素の値を255で割ります。**訓練用データセット**と**テスト用データセット**は、同じように前処理することが重要です。 | train_images = train_images / 255.0
test_images = test_images / 255.0 | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
**訓練用データセット**の最初の25枚の画像を、クラス名付きで表示してみましょう。ネットワークを構築・訓練する前に、データが正しいフォーマットになっていることを確認します。 | plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])
plt.show() | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
モデルの構築ニューラルネットワークを構築するには、まずモデルの階層を定義し、その後モデルをコンパイルします。 層の設定ニューラルネットワークを形作る基本的な構成要素は**層**（layer）です。層は、入力されたデータから「表現」を抽出します。それらの「表現」は、今取り組もうとしている問題に対して、より「意味のある」ものであることが期待されます。ディープラーニングモデルのほとんどは、単純な層の積み重ねで構成されています。`tf.keras.layers.Dense` のような層のほとんどには、訓練中に学習されるパラメータが存在します。 | model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
]) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
このネットワークの最初の層は、`tf.keras.layers.Flatten` です。この層は、画像を（28×28ピクセルの）2次元配列から、28×28＝784ピクセルの、1次元配列に変換します。この層が、画像の中に積まれているピクセルの行を取り崩し、横に並べると考えてください。この層には学習すべきパラメータはなく、ただデータのフォーマット変換を行うだけです。ピクセルが１次元化されたあと、ネットワークは2つの `tf.keras.layers.Dense` 層となります。これらの層は、密結合あるいは全結合されたニューロンの層となります。最初の `Dense` 層には、128個のノード（あるはニューロン）があります。最後の層でも... | model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
モデルの訓練ニューラルネットワークの訓練には次のようなステップが必要です。1. モデルに訓練用データを投入します—この例では `train_images` と `train_labels` の２つの配列です。2. モデルは、画像とラベルの対応関係を学習します。3. モデルにテスト用データセットの予測（分類）を行わせます—この例では `test_images` 配列です。その後、予測結果と `test_labels` 配列を照合します。 訓練を開始するには、`model.fit` メソッドを呼び出します。モデルを訓練用データに "fit"（適合）させるという意味です。 | model.fit(train_images, train_labels, epochs=5) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
モデルの訓練の進行とともに、損失値と正解率が表示されます。このモデルの場合、訓練用データでは0.88（すなわち88%）の正解率に達します。 正解率の評価次に、テスト用データセットに対するモデルの性能を比較します。 | test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
ご覧の通り、テスト用データセットでの正解率は、訓練用データセットでの正解率よりも少し低くなります。この訓練時の正解率とテスト時の正解率の差は、**過学習**（over fitting）の一例です。過学習とは、新しいデータに対する機械学習モデルの性能が、訓練時と比較して低下する現象です。 予測するモデルの訓練が終わったら、そのモデルを使って画像の分類予測を行うことが出来ます。 | predictions = model.predict(test_images) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
これは、モデルがテスト用データセットの画像のひとつひとつを分類予測した結果です。最初の予測を見てみましょう。 | predictions[0] | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
予測結果は、10個の数字の配列です。これは、その画像が10の衣料品の種類のそれぞれに該当するかの「確信度」を表しています。どのラベルが一番確信度が高いかを見てみましょう。 | np.argmax(predictions[0]) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
というわけで、このモデルは、この画像が、アンクルブーツ、`class_names[9]` である可能性が最も高いと判断したことになります。これが正しいかどうか、テスト用ラベルを見てみましょう。 | test_labels[0] | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
10チャンネルすべてをグラフ化してみることができます。 | def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == tru... | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
0番目の画像と、予測、予測配列を見てみましょう。 | i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)
plt.show()
i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_arra... | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
予測の中のいくつかの画像を、予測値とともに表示してみましょう。正しい予測は青で、誤っている予測は赤でラベルを表示します。数字は予測したラベルのパーセント（100分率）を示します。自信があるように見えても間違っていることがあることに注意してください。 | # X個のテスト画像、予測されたラベル、正解ラベルを表示します。
# 正しい予測は青で、間違った予測は赤で表示しています。
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(num_rows, 2*nu... | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
最後に、訓練済みモデルを使って1枚の画像に対する予測を行います。 | # テスト用データセットから画像を1枚取り出す
img = test_images[0]
print(img.shape) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
`tf.keras` モデルは、サンプルの中の**バッチ**（batch）あるいは「集まり」について予測を行うように作られています。そのため、1枚の画像を使う場合でも、リスト化する必要があります。 | # 画像を1枚だけのバッチのメンバーにする
img = (np.expand_dims(img,0))
print(img.shape) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
そして、予測を行います。 | predictions_single = model.predict(img)
print(predictions_single)
plot_value_array(0, predictions_single, test_labels)
_ = plt.xticks(range(10), class_names, rotation=45) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
`model.predict` メソッドの戻り値は、リストのリストです。リストの要素のそれぞれが、バッチの中の画像に対応します。バッチの中から、（といってもバッチの中身は１つだけですが）予測を取り出します。 | np.argmax(predictions_single[0]) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/keras/classification.ipynb | KarimaTouati/docs-l10n |
PA005: High Value Customer Identification (Insiders) Ciclo 0 - Planejamento da solução (IOT) Ciclo 1 - Métricas de Validação de Clusters 1. Feature Engineering* Recency* Frequency* Monetary2. Métricas de Validação de clusters* WSS - (Within-Cluster Sum of Squares)* SS - (Silhouette Score)3. Cluster Analisys* Plot ... |
from yellowbrick.cluster import KElbowVisualizer
from yellowbrick.cluster import SilhouetteVisualizer
from matplotlib import pyplot as plt
from sklearn import cluster as c
from sklearn import metrics as m
from sklearn import preprocessing as pp
from plotly import express as ... | /home/heitor/repos/insiders_clustering/venv/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
| MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
0.2. Helper Functions | pd.set_option('display.float_format', lambda x: '%.2f' % x)
def num_attributes(df1):
num_attributes = df1.select_dtypes(['int64', 'float64'])
#central tendency
ct1 = pd.DataFrame(num_attributes.apply(np.mean)).T
ct2 = pd.DataFrame(num_attributes.apply(np.median)).T
#dispersion
d1 = pd.Da... | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
0.3. Load Data | df_raw = pd.read_csv(r'../data/Ecommerce.csv')
| _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.0. Data Description | df1 = df_raw.copy() | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.1. Rename Columns | df1.columns
df1.columns = ['invoice_no', 'stock_code', 'description', 'quantity', 'invoice_date',
'unit_price', 'customer_id', 'country'] | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.2. Data Shape | print(f'Number of rows: {df1.shape[0]}')
print(f'Number of columns: {df1.shape[1]}') | Number of rows: 541909
Number of columns: 8
| MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.3. Data Types | df1.dtypes | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.4. Check NAs | df1.isna().sum() | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.5. Fill NAs | #remove na
df1 = df1.dropna(axis=0)
print('Data removed: {:.0f}%'.format((1-(len(df1)/len(df_raw)))*100)) | Data removed: 25%
| MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.6. Change dtypes | df1.dtypes
#invoice_no
# df1['invoice_no'] = df1['invoice_no'].astype(int)
#stock_code
# df1['stock_code'] = df1['stock_code'].astype(int)
#invoice_date --> Month --> b
df1['invoice_date'] = pd.to_datetime(df1['invoice_date'], format=('%d-%b-%y'))
#customer_id
df1['customer_id'] = df1['customer_id'].astype(int... | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.7. Descriptive statistics | num_attributes = df1.select_dtypes(['int64', 'float64'])
cat_attributes = df1.select_dtypes(exclude = ['int64', 'float64', 'datetime64[ns]']) | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.7.1. Numerical Attributes | m = num_attributes(df1)
m | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
1.7.1.1 Investigating 1. Negative quantity (devolution?)2. Price = 0 (Promo?) 1.7.2. Categorical Attributes | cat_attributes.head() | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
Invoice no |
#invoice_no -- some of them has one char
df_invoice_char = df1.loc[df1['invoice_no'].apply(lambda x: bool(re.search('[^0-9]+', x))), :]
len(df_invoice_char[df_invoice_char['quantity']<0])
print('Total of invoices with letter: {}'.format(len(df_invoice_char)))
print('Total of negative quantaty: {}'.format(len(df1... | _____no_output_____ | MIT | notebooks/code3.ipynb | heitorfe/insiders_clustering |
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