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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RecordID (a unique integer for each ICU stay)Age (years)Gender (0: female, or 1: male)Height (cm)ICUType (1: Coronary Care Unit, 2: Cardiac Surgery Recovery Unit, 3: Medical ICU, or 4: Surgical ICU)Weight (kg) Variables Description ALB Albumin (g/dL) ALP Alkaline phosphatase (IU/L) ALT Alanine transaminase (IU/L) AST A... | # Open file
with open('./training_set_a/132539.txt') as inputfile:
results = list(csv.reader(inputfile)) # Open file in list of list
results = pd.DataFrame(results[1:],columns=results[0]) # Convert list of list to DataFrame
results.Value = results.Value.astype(float) # Change Value t... | _____no_output_____ | CC-BY-3.0 | __Project Files/.ipynb_checkpoints/Data Cleaning_merge all data together_backup-checkpoint.ipynb | joannasys/Predictions-of-ICU-Mortality |
EDA 1. Check if the data is unbalanced | # Open outcomes file
with open('./training_outcomes_a.txt') as outcomefile:
# Open file in list of list
outcome = list(csv.reader(outcomefile))
outcome = pd.DataFrame(outcome[1:],columns=outcome[0]) # Convert list of list to DataFrame
outcome = outcome.astype(float,'ignore') # Change value... | percentage of 0 in dataset: 86.15 %
percentage of 1 in dataset: 13.85 %
| CC-BY-3.0 | __Project Files/.ipynb_checkpoints/Data Cleaning_merge all data together_backup-checkpoint.ipynb | joannasys/Predictions-of-ICU-Mortality |
2. Create outcomes table in database | outcome.head(5)
pd.to_sql() | _____no_output_____ | CC-BY-3.0 | __Project Files/.ipynb_checkpoints/Data Cleaning_merge all data together_backup-checkpoint.ipynb | joannasys/Predictions-of-ICU-Mortality |
Copyright 2018 The TensorFlow Authors.Licensed under the Apache License, Version 2.0 (the "License"). Neural Machine Translation with Attention Run in Google Colab View source on GitHub This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tenso... | from __future__ import absolute_import, division, print_function
# Import TensorFlow >= 1.9 and enable eager execution
import tensorflow as tf
tf.enable_eager_execution()
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import unicodedata
import re
import numpy as np
import os
im... | _____no_output_____ | Apache-2.0 | notebooks/eager/nmt_w_attention.ipynb | cnodadiaz/tf-workshop |
Download and prepare the datasetWe'll use a language dataset provided by http://www.manythings.org/anki/. This dataset contains language translation pairs in the format:```May I borrow this book? ¿Puedo tomar prestado este libro?```There are a variety of languages available, but we'll use the English-Spanish dataset. ... | # Download the file
path_to_zip = tf.keras.utils.get_file(
'spa-eng.zip', origin='http://download.tensorflow.org/data/spa-eng.zip',
extract=True)
path_to_file = os.path.dirname(path_to_zip)+"/spa-eng/spa.txt"
# Converts the unicode file to ascii
def unicode_to_ascii(s):
return ''.join(c for c in unicodeda... | _____no_output_____ | Apache-2.0 | notebooks/eager/nmt_w_attention.ipynb | cnodadiaz/tf-workshop |
Limit the size of the dataset to experiment faster (optional)Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data): | # Try experimenting with the size of that dataset
num_examples = 30000
input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_targ = load_dataset(path_to_file, num_examples)
# Creating training and validation sets using an 80-20 split
input_tensor_train, input_tensor_val, target_tensor_train, targ... | _____no_output_____ | Apache-2.0 | notebooks/eager/nmt_w_attention.ipynb | cnodadiaz/tf-workshop |
Create a tf.data dataset | BUFFER_SIZE = len(input_tensor_train)
BATCH_SIZE = 64
N_BATCH = BUFFER_SIZE//BATCH_SIZE
embedding_dim = 256
units = 1024
vocab_inp_size = len(inp_lang.word2idx)
vocab_tar_size = len(targ_lang.word2idx)
dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)
dataset ... | _____no_output_____ | Apache-2.0 | notebooks/eager/nmt_w_attention.ipynb | cnodadiaz/tf-workshop |
Write the encoder and decoder modelHere, we'll implement an encoder-decoder model with attention which you can read about in the TensorFlow [Neural Machine Translation (seq2seq) tutorial](https://www.tensorflow.org/tutorials/seq2seq). This example uses a more recent set of APIs. This notebook implements the [attention... | def gru(units):
# If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)
# the code automatically does that.
if tf.test.is_gpu_available():
return tf.keras.layers.CuDNNGRU(units,
return_sequences=True,
return_sta... | _____no_output_____ | Apache-2.0 | notebooks/eager/nmt_w_attention.ipynb | cnodadiaz/tf-workshop |
Define the optimizer and the loss function | optimizer = tf.train.AdamOptimizer()
def loss_function(real, pred):
mask = 1 - np.equal(real, 0)
loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask
return tf.reduce_mean(loss_) | _____no_output_____ | Apache-2.0 | notebooks/eager/nmt_w_attention.ipynb | cnodadiaz/tf-workshop |
Training1. Pass the *input* through the *encoder* which return *encoder output* and the *encoder hidden state*.2. The encoder output, encoder hidden state and the decoder input (which is the *start token*) is passed to the decoder.3. The decoder returns the *predictions* and the *decoder hidden state*.4. The decoder h... | EPOCHS = 10
for epoch in range(EPOCHS):
start = time.time()
hidden = encoder.initialize_hidden_state()
total_loss = 0
for (batch, (inp, targ)) in enumerate(dataset):
loss = 0
with tf.GradientTape() as tape:
enc_output, enc_hidden = encoder(inp, hidden)
... | _____no_output_____ | Apache-2.0 | notebooks/eager/nmt_w_attention.ipynb | cnodadiaz/tf-workshop |
Translate* The evaluate function is similar to the training loop, except we don't use *teacher forcing* here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.* Stop predicting when the model predicts the *end token*.* And store the *attention we... | def evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):
attention_plot = np.zeros((max_length_targ, max_length_inp))
sentence = preprocess_sentence(sentence)
inputs = [inp_lang.word2idx[i] for i in sentence.split(' ')]
inputs = tf.keras.preprocessing.sequenc... | _____no_output_____ | Apache-2.0 | notebooks/eager/nmt_w_attention.ipynb | cnodadiaz/tf-workshop |
Job parameters | BUCKET="tanmcrae-greengrass-blog"
bucket_region = s3.head_bucket(Bucket=BUCKET)['ResponseMetadata']['HTTPHeaders']['x-amz-bucket-region']
assert bucket_region == region, "Your S3 bucket {} and this notebook need to be in the same region.".format(BUCKET)
MANIFEST = "blue_box_large_job.json"
JOB_NAME = "blue-box-large-jo... | _____no_output_____ | MIT-0 | data-prep/04_create_ground_truth_job.ipynb | jonslo/amazon-sagemaker-aws-greengrass-custom-object-detection-model |
specifying categories | CLASS_NAME = "storage box"
CLASS_LIST = [CLASS_NAME]
print("Label space is {}".format(CLASS_LIST))
json_body = {
'labels': [{'label': label} for label in CLASS_LIST]
}
with open('class_labels.json', 'w') as f:
json.dump(json_body, f)
LABEL_KEY = "ground-truth/{}/class_labels.json".format(EXP_NAME)
s3.upload_f... | Label space is ['storage box']
uploaded s3://tanmcrae-greengrass-blog/ground-truth/blue-box/class_labels.json
| MIT-0 | data-prep/04_create_ground_truth_job.ipynb | jonslo/amazon-sagemaker-aws-greengrass-custom-object-detection-model |
Create the instruction template | def make_template(test_template=False, save_fname='instructions.template'):
template = r"""<script src="https://assets.crowd.aws/crowd-html-elements.js"></script>
<crowd-form>
<crowd-bounding-box
name="boundingBox"
src="{{ task.input.taskObject | grant_read_access }}"
header="Draw bounding box for th... | _____no_output_____ | MIT-0 | data-prep/04_create_ground_truth_job.ipynb | jonslo/amazon-sagemaker-aws-greengrass-custom-object-detection-model |
Create job | task_description = 'Dear Annotator, please draw a box around the yellow or blue storage box in the picture. Thank you!'
task_keywords = ['image', 'object', 'detection', CLASS_NAME]
task_title = 'Draw a box around storage box in the picture'
print("task_title: {}".format(task_title))
print("JOB_NAME: {}".format(JOB_NAM... | _____no_output_____ | MIT-0 | data-prep/04_create_ground_truth_job.ipynb | jonslo/amazon-sagemaker-aws-greengrass-custom-object-detection-model |
look at output manifest | job_name = 'yellow-box-small-job-public'
OUTPUT_MANIFEST = 's3://{}/ground-truth-output/{}/manifests/output/output.manifest'.format(BUCKET, job_name)
output_file = job_name+'.output.manifest'
!aws s3 cp {OUTPUT_MANIFEST} {output_file}
with open(output_file, 'r') as f:
output = [json.loads(line.strip()) for line in... | _____no_output_____ | MIT-0 | data-prep/04_create_ground_truth_job.ipynb | jonslo/amazon-sagemaker-aws-greengrass-custom-object-detection-model |
synchro.io> IO classes to read files in the different format Asari lab is acquiring from: hdf5, rhd, raw, npy, all adapted from the SpykingCircus project by Pierre Yger and Olivier Marre https://spyking-circus.readthedocs.io/en/latest/ | #export
import numpy as np
import re, sys, os, logging, struct
import h5py
from colorama import Fore
logger = logging.getLogger(__name__)
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
'''
alist.sort(key=natural_keys) sorts in human order
http://nedbatchelder.com... | Converted 00_core.ipynb.
Converted 01_utils.ipynb.
Converted 02_processing.ipynb.
Converted 03_modelling.ipynb.
Converted 04_plotting.ipynb.
Converted 05_database.ipynb.
Converted 06_eyetrack.ipynb.
Converted 10_synchro.io.ipynb.
Converted 11_synchro.extracting.ipynb.
Converted 12_synchro.processing.ipynb.
Converted 13... | Apache-2.0 | 10_synchro.io.ipynb | wiessall/theonerig |
Visit the NASA mars news site | # Visit the Mars news site
url = 'https://redplanetscience.com/'
browser.visit(url)
# Optional delay for loading the page
browser.is_element_present_by_css('div.list_text', wait_time=1)
# Convert the browser html to a soup object
html = browser.html
news_soup = soup(html, 'html.parser')
slide_elem = news_soup.select_... | _____no_output_____ | ADSL | Mission_to_Mars-Starter.ipynb | ptlhrs7/Web-Scraping-and-Mongo-Homework |
JPL Space Images Featured Image | # Visit URL
url = 'https://spaceimages-mars.com'
browser.visit(url)
# Find and click the full image button
full_image_link = browser.find_by_tag('button')[1]
full_image_link.click()
# Parse the resulting html with soup
html = browser.html
img_soup = soup(html, 'html.parser')
#print(news_soup.prettify())
img_url_rel = i... | _____no_output_____ | ADSL | Mission_to_Mars-Starter.ipynb | ptlhrs7/Web-Scraping-and-Mongo-Homework |
Mars Facts | # Use `pd.read_html` to pull the data from the Mars-Earth Comparison section
# hint use index 0 to find the table
df = pd.read_html("https://galaxyfacts-mars.com/")[0]
df.head()
df.columns = ['Description', 'Mars', 'Earth']
df
df.set_index('Description', inplace=True)
df.to_html() | _____no_output_____ | ADSL | Mission_to_Mars-Starter.ipynb | ptlhrs7/Web-Scraping-and-Mongo-Homework |
Hemispheres | url = 'https://marshemispheres.com/'
browser.visit(url)
# Create a list to hold the images and titles.
hemisphere_image_urls = []
# Get a list of all of the hemispheres
links = browser.find_by_css('a.product-item img')
# Next, loop through those links, click the link, find the sample anchor, return the href
for i ... | _____no_output_____ | ADSL | Mission_to_Mars-Starter.ipynb | ptlhrs7/Web-Scraping-and-Mongo-Homework |
FactRuEval example (Cased model), MutiHeadAttention | %reload_ext autoreload
%autoreload 2
%matplotlib inline
import warnings
import sys
sys.path.append("../")
warnings.filterwarnings("ignore")
import os
data_path = "/home/lis/ner/ulmfit/data/factrueval/"
train_path = os.path.join(data_path, "train_with_pos.csv")
valid_path = os.path.join(data_path, "valid_with_pos.c... | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
1. Create dataloaders | from modules import BertNerData as NerData
data = NerData.create(train_path, valid_path, vocab_file) | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
For factrueval we use the following sample of labels: | print(data.label2idx) | {'<pad>': 0, '[CLS]': 1, '[SEP]': 2, 'B_O': 3, 'I_O': 4, 'B_ORG': 5, 'I_ORG': 6, 'B_LOC': 7, 'I_LOC': 8, 'B_PER': 9, 'I_PER': 10}
| MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
2. Create modelFor creating pytorch model we need to create `NerModel` object. | from modules.models.bert_models import BertBiLSTMAttnCRF
model = BertBiLSTMAttnCRF.create(len(data.label2idx), bert_config_file, init_checkpoint_pt, enc_hidden_dim=256)
model.decoder
model.get_n_trainable_params() | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
3. Create learnerFor training our pytorch model we need to create `NerLearner` object. | from modules import NerLearner
num_epochs = 100
learner = NerLearner(model, data,
best_model_path="/datadrive/models/factrueval/exp_final_attn_cased1.cpt",
lr=0.001, clip=1.0, sup_labels=data.id2label[5:],
t_total=num_epochs * len(data.train_dl)) | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
4. Learn your NER modelCall `learner.fit` | learner.fit(num_epochs, target_metric='f1') | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
5. EvaluateCreate new data loader from existing path. | from modules.data.bert_data import get_bert_data_loader_for_predict
dl = get_bert_data_loader_for_predict(data_path + "valid.csv", learner)
learner.load_model()
preds = learner.predict(dl) | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
IOB precision | from modules.train.train import validate_step
print(validate_step(learner.data.valid_dl, learner.model, learner.data.id2label, learner.sup_labels)) | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
Tokens report | from sklearn_crfsuite.metrics import flat_classification_report
from modules.utils.utils import bert_labels2tokens
pred_tokens, pred_labels = bert_labels2tokens(dl, preds)
true_tokens, true_labels = bert_labels2tokens(dl, [x.labels for x in dl.dataset])
assert pred_tokens == true_tokens
tokens_report = flat_classificat... | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
Span precision | from modules.utils.utils import voting_choicer
print(get_bert_span_report(dl, preds, fn=voting_choicer)) | precision recall f1-score support
ORG 0.809 0.834 0.821 259
LOC 0.851 0.859 0.855 192
PER 0.936 0.936 0.936 188
micro avg 0.858 0.872 0.865 639
macro avg 0.865 0.877 0.871 ... | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
6. Get mean and stdv on 10 runs | from modules.utils.plot_metrics import *
num_runs = 10
best_reports = []
try:
for i in range(num_runs):
model = BertBiLSTMAttnCRF.create(len(data.label2idx), bert_config_file, init_checkpoint_pt, enc_hidden_dim=256)
best_model_path = "/datadrive/models/factrueval/exp_{}_attn_cased.cpt".format(i)
... | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
f1 Mean and std | np.mean([get_mean_max_metric([r]) for r in best_reports]), np.round(np.std([get_mean_max_metric([r]) for r in best_reports]), 3) | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
Best | get_mean_max_metric(best_reports) | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
precision Mean and std | np.mean([get_mean_max_metric([r], "prec") for r in best_reports]), np.round(np.std([get_mean_max_metric([r], "prec") for r in best_reports]), 3) | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
Best | get_mean_max_metric(best_reports, "prec") | _____no_output_____ | MIT | examples/factrueval.ipynb | sloth2012/ner-bert |
NLP_Session_2_AmazonReviews_Example Amazon Review Polarity Dataset DOWNLOAD DATA FROM HEREhttps://www.kaggle.com/kritanjalijain/amazon-reviews/download Extract the train and test CSV files to your google drive location and configure that in the code OVERVIEWContains 34,686,770 Amazon reviews from 6,643,669 users on 2,... | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tarfile
import seaborn as sns
import matplotlib.style as style
import matplotlib as mpl
import re
import string
import itertools
import collections
from wordcloud import WordCloud
import nltk
from nltk.util import ngrams
from nltk.corpus im... | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Setting up NLP Libraries and corpus | nltk.download('stopwords')
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger') | [nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data] Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data] Package punkt is already up-to-date!
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data] /root/nltk_d... | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Mounting the data source | from google.colab import drive
drive.mount('/content/drive',force_remount=True) | Mounted at /content/drive
| MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Configuring the input, output and process folders | # Model Input and output folders
# Setup in Google drive
# '/content/drive/MyDrive/yourlocation/input/'
model_input_folder='/content/drive/MyDrive/yourlocation/input/'
model_output_folder='/content/drive/MyDrive/yourlocation/output/'
input_train_file=model_input_folder+'train.csv'
input_test_file=model_input_folder+'te... | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Loading the training dataset | # check out what the data looks like before you get started
# look at the training data set
train_df = pd.read_csv(input_train_file, header=None)
print(train_df.head())
train_df.shape | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Reducing the size of the dataframe for demonstration purposes | # Reducing the size of the dataframe
train_df=train_df.loc[1:10000]
for col in train_df.columns:
print(col)
#checking a null values
train_df.isnull().sum()
#droping null vlaues
train_df.dropna()
train_df.isnull().count() | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Quick look at the dataset loaded | train_df.info()
train_df.drop([0],axis=1,inplace=True)
for col in train_df.columns:
print(col)
train_df.drop([1],axis=1,inplace=True)
train_df.shape
train_df.sample(5)
# Checking for Null Values
train_df[train_df[2].isnull()] | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Obtaining the review lengths | train = train_df.copy()
train[2].apply(str)
train["review_length"] = train[2].apply(lambda w : len(re.findall(r'\w+', w))) | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Getting some statistics around the review length | train['review_length'].describe() | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Doing some graphical plots | sns.boxplot(data = train , x="review_length")
plt.xlabel('Number of Words')
plt.title('Review Length, Including Stop Words')
plt.show()
sns.distplot(train['review_length'], kde = False)
plt.xlabel('Distribution of Review Length')
plt.title('Review Length, Including Stop Words')
plt.show() | /usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
war... | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
If we want to do better we must pre-process data like1. Converting to lower case2. Removing punctuation3. Removing Numbers4. Removing trailing spaces5. Removing extra whitespaces | train_clean = train.copy()
stop_words = stopwords.words("english") | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Function to clean text | # Function for cleaning text
def clean(s):
s = s.lower() #Converting to lower case
s = re.sub(r'[^\w\s]', ' ', s) #Removing punctuation
s = re.sub(r'[\d+]', ' ', s) #Removing Numbers
s = s.strip() #Removing trailing spaces
s = re.sub(' +', ' ', s) #Remo... | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
1. Removal of STOP WORDS | # Removal of Stop Words
train_clean["Reviews"] = train_clean["Reviews"].apply(lambda x: " ".join(x for x in x.split() if x not in stop_words))
import pandas as pd
reviews = pd.Series(train_clean["Reviews"].tolist()).astype(str)
plt.figure(figsize = (9, 9))
rev_wcloud_all = WordCloud(width = 900, height = 900, colormap ... | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
Detailed NLP Analysis | tokenizer = RegexpTokenizer(r'\w+')
train_clean["review_token"] = train_clean["Reviews"].apply(lambda x: tokenizer.tokenize(x))
# Sentiment analysis
train_clean["sentiment_polarity"] = train_clean["Reviews"].apply(lambda x: TextBlob(x).sentiment.polarity)
train_clean["sentiment_subjectivity"] = train_clean["Reviews"].a... | _____no_output_____ | MIT | AmazonReviews_Example/NLP_Session_2_AmazonReviews_Example.ipynb | drshyamsundaram/nlp |
 2. Text Preprocessing with Spark NLP **Note** Read this article if you want to understand the basic concepts in Spark NLP.https://towardsdatascience.com/introduction-to-spark-nlp-foundations-and-basic-components-part-i-c83b7629ed59 1. Annotators and... | import sparknlp
spark = sparknlp.start()
print("Spark NLP version", sparknlp.version())
print("Apache Spark version:", spark.version) | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Create Spark Dataframe | text = 'Peter Parker is a nice guy and lives in New York'
spark_df = spark.createDataFrame([[text]]).toDF("text")
spark_df.show(truncate=False)
# if you want to create a spark datafarme from a list of strings
from pyspark.sql.types import StringType
text_list = ['Peter Parker is a nice guy and lives in New York.', 'B... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Transformers what are we going to do if our DataFrame doesn’t have columns in those type? Here comes transformers. In Spark NLP, we have five different transformers that are mainly used for getting the data in or transform the data from one AnnotatorType to another. Here is the list of transformers:`DocumentAssembler`... | from sparknlp.base import *
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")\
.setCleanupMode("shrink")
doc_df = documentAssembler.transform(spark_df)
doc_df.show(truncate=30) | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
At first, we define DocumentAssembler with desired parameters and then transform the data frame with it. The most important point to pay attention to here is that you need to use a String or String[Array] type column in .setInputCol(). So it doesn’t have to be named as text. You just use the column name as it is. | doc_df.printSchema()
doc_df.select('document.result','document.begin','document.end').show(truncate=False)
| _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
The new column is in an array of struct type and has the parameters shown above. The annotators and transformers all come with universal metadata that would be filled down the road depending on the annotators being used. Unless you want to append other Spark NLP annotators to DocumentAssembler(), you don’t need to know... | doc_df.select("document.result").take(1) | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
If we would like to flatten the document column, we can do as follows. | import pyspark.sql.functions as F
doc_df.withColumn(
"tmp",
F.explode("document"))\
.select("tmp.*")\
.show(truncate=False) | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
3. Sentence Detector Finds sentence bounds in raw text. `setCustomBounds(string)`: Custom sentence separator text`setUseCustomOnly(bool)`: Use only custom bounds without considering those of Pragmatic Segmenter. Defaults to false. Needs customBounds.`setUseAbbreviations(bool)`: Whether to consider abbreviation strateg... | from sparknlp.annotator import *
# we feed the document column coming from Document Assembler
sentenceDetector = SentenceDetector().\
setInputCols(['document']).\
setOutputCol('sentences')
sent_df = sentenceDetector.transform(doc_df)
sent_df.show(truncate=False)
sent_df.select('sentences').take(3)
text ='The patien... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Tokenizer Identifies tokens with tokenization open standards. It is an **Annotator Approach, so it requires .fit()**.A few rules will help customizing it if defaults do not fit user needs.setExceptions(StringArray): List of tokens to not alter at all. Allows composite tokens like two worded tokens that the user may no... | tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
text = 'Peter Parker (Spiderman) is a nice guy and lives in New York but has no e-mail!'
spark_df = spark.createDataFrame([[text]]).toDF("text")
doc_df = documentAssembler.transform(spark_df)
token_df = tokenizer.fit(doc_df).trans... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Stacking Spark NLP Annotators in Spark ML Pipeline Spark NLP provides an easy API to integrate with Spark ML Pipelines and all the Spark NLP annotators and transformers can be used within Spark ML Pipelines. So, it’s better to explain Pipeline concept through Spark ML official documentation.What is a Pipeline anyway? ... | from pyspark.ml import Pipeline
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetector().\
setInputCols(['document']).\
setOutputCol('sentences')
tokenizer = Tokenizer() \
.setInputCols(["sentences"]) \
.setOutputCol("token")
nlpPipeline ... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Normalizer Removes all dirty characters from text following a regex pattern and transforms words based on a provided dictionary`setCleanupPatterns(patterns)`: Regular expressions list for normalization, defaults [^A-Za-z]`setLowercase(value)`: lowercase tokens, default false`setSlangDictionary(path)`: txt file with de... | import string
string.punctuation
from sparknlp.annotator import *
from sparknlp.base import *
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
normalizer = Normalizer() \
.setInputCols([... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Stopwords Cleaner This annotator excludes from a sequence of strings (e.g. the output of a Tokenizer, Normalizer, Lemmatizer, and Stemmer) and drops all the stop words from the input sequences. Functions:`setStopWords`: The words to be filtered out. Array[String]`setCaseSensitive`: Whether to do a case sensitive compa... | stopwords_cleaner = StopWordsCleaner()\
.setInputCols("token")\
.setOutputCol("cleanTokens")\
.setCaseSensitive(False)\
#.setStopWords(["no", "without"]) (e.g. read a list of words from a txt)
stopwords_cleaner.getStopWords()
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOu... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Token Assembler | documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetector().\
setInputCols(['document']).\
setOutputCol('sentences')
tokenizer = Tokenizer() \
.setInputCols(["sentences"]) \
.setOutputCol("token")
normalizer = Normalizer() \
.setI... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Stemmer Returns hard-stems out of words with the objective of retrieving the meaningful part of the word | stemmer = Stemmer() \
.setInputCols(["token"]) \
.setOutputCol("stem")
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
nlpPipeline = Pipeline(stages=[
documentAssembler,
tokenizer... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Lemmatizer Retrieves lemmas out of words with the objective of returning a base dictionary word | !wget https://raw.githubusercontent.com/mahavivo/vocabulary/master/lemmas/AntBNC_lemmas_ver_001.txt -P /FileStore/
lemmatizer = Lemmatizer() \
.setInputCols(["token"]) \
.setOutputCol("lemma") \
.setDictionary("/FileStore/AntBNC_lemmas_ver_001.txt", value_delimiter ="\t", key_delimiter = "->")
documentAssem... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
NGram Generator NGramGenerator annotator takes as input a sequence of strings (e.g. the output of a `Tokenizer`, `Normalizer`, `Stemmer`, `Lemmatizer`, and `StopWordsCleaner`). The parameter n is used to determine the number of terms in each n-gram. The output will consist of a sequence of n-grams where each n-gram is... | ngrams_cum = NGramGenerator() \
.setInputCols(["token"]) \
.setOutputCol("ngrams") \
.setN(3) \
.setEnableCumulative(True)\
.setDelimiter("_") # Default is space
# .setN(3) means, take bigrams and trigrams.
nlpPipeline = Pipeline(stages=[
documentAssemb... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
TextMatcher Annotator to match entire phrases (by token) provided in a file against a DocumentFunctions:setEntities(path, format, options): Provides a file with phrases to match. Default: Looks up path in configuration.path: a path to a file that contains the entities in the specified format.readAs: the format of the ... | # first method for doing this, second option below
import urllib.request
with urllib.request.urlopen('https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/pubmed/pubmed-sample.csv') as f:
content = f.read().decode('utf-8')
dbutils.fs.put("/dbfs/tmp/pubmed/pubmed-sample.csv", content)
%sh
TMP=/... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
RegexMatcher | rules = '''
renal\s\w+, started with 'renal'
cardiac\s\w+, started with 'cardiac'
\w*ly\b, ending with 'ly'
\S*\d+\S*, match any word that contains numbers
(\d+).?(\d*)\s*(mg|ml|g), match medication metrics
'''
dbutils.fs.put("dbfs:/tmp/pubmed/regex_rules.txt", rules)
import os
documentAssembler = DocumentAssembler()... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Text Cleaning with UDF | text = '<h1 style="color: #5e9ca0;">Have a great <span style="color: #2b2301;">birth</span> day!</h1>'
text_df = spark.createDataFrame([[text]]).toDF("text")
import re
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType, IntegerType
clean_text = lambda s: re.sub(r'<[^>]*>', '', s)
text_d... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
Finisher ***Finisher:*** Once we have our NLP pipeline ready to go, we might want to use our annotation results somewhere else where it is easy to use. The Finisher outputs annotation(s) values into a string.If we just want the desired output column in the final dataframe, we can use Finisher to drop previous stages i... | finisher = Finisher() \
.setInputCols(["regex_matches"]) \
.setIncludeMetadata(False) # set to False to remove metadata
nlpPipeline = Pipeline(stages=[
documentAssembler,
regex_matcher,
finisher
])
empty_df = spark.createDataFrame([['']]).toDF("text")
pipelineModel = nlpPipeline.fit(empty_df)
match_df ... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
LightPipeline LightPipelines are Spark NLP specific Pipelines, equivalent to Spark ML Pipeline, but meant to deal with smaller amounts of data. They’re useful working with small datasets, debugging results, or when running either training or prediction from an API that serves one-off requests.Spark NLP LightPipelines ... | documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
stemmer = Stemmer() \
.setInputCols(["token"]) \
.setOutputCol("stem")
nlpPipeline = Pipeline(stages=[
documentAssembler,
tokenize... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/databricks_notebooks/2.4/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb | hatrungduc/spark-nlp-workshop |
How to deliver JavaScript to the IPython Notebook ViewerAt first glance there appear to be at least four mechanismsfor adding JavaScript code to an IPython notebook: * A notebook cell marked `%%javascript`* A Markdown cell with a `` inside* An `HTML()` display with a `` inside* A `JavaScript()` display with code in... | %%javascript
console.log('Log message from the %%javascript cell') | _____no_output_____ | MIT | Javascript-integration.ipynb | Grant-Steinfeld/astronomy-notebooks |
*(Markdown cell with a `` inside.)*console.log('Log message from the Markdown cell') | from IPython.display import HTML
HTML('<script>console.log("Log message from an HTML display")</script>')
from IPython.display import Javascript
Javascript('console.log("Log message from a Javascript display")') | _____no_output_____ | MIT | Javascript-integration.ipynb | Grant-Steinfeld/astronomy-notebooks |
Playing around with NLTKSome material has been taken/adapted from the [NLTK book](http://www.nltk.org/book/)* Exploring NLTK books (Text instance)* Exploring NLTK corpora* Exploring NLTK Treebank* Exploring the WordNet corpusFor the linguistics concepts used here, refer to [the specific notebook](../nlp/concepts/lingu... | ## List of all the books and sents imported
texts()
sents()
# Choose the book to play with and some wordsText
book = text2
word = 'love'
word2 = 'him'
words = ['love', 'kiss', 'marriage', 'sense', 'children', 'house', 'hate']
# Print first 100 token in book (book is an instance of nltk.text.Text, which behaves like a... | All genres in Brown corpus: ['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor', 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']
| MIT | toolbox/python/nltk.ipynb | martinapugliese/tales-science-data-notebooks |
Treebank* Parsed sentences | # The Treebank corpus in NLTK contains 10% of the original Penn Treebank corpus
treebank.words()
treebank.parsed_sents() | _____no_output_____ | MIT | toolbox/python/nltk.ipynb | martinapugliese/tales-science-data-notebooks |
WordNet* Hypernyms and Hyponyms | wn = wordnet
sss = wn.synsets('dog')
s1 = sss[0]
print(s1, s1.definition())
print(s1.hypernyms(), s1.hyponyms()) | Synset('dog.n.01') a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds
[Synset('canine.n.02'), Synset('domestic_animal.n.01')] [Synset('basenji.n.01'), Synset('corgi.n.01'), Synset('cur.n.01'), Synset('dalmatian.n.02'), S... | MIT | toolbox/python/nltk.ipynb | martinapugliese/tales-science-data-notebooks |
Text manipulation* Tokenizing* POS tagging* Stemming/lemmatizing | # tagged sentences from Brown corpus
brown_tagged_sents = brown.tagged_sents(categories='news')
# Separate tagged sents into train and test
train_sents = brown_tagged_sents[:int(len(brown_tagged_sents) * 0.8)]
test_sents = brown_tagged_sents[int(len(brown_tagged_sents) * 0.8):]
# Tokenising
# NOTE: obvs the easiest se... | mice: mouse
| MIT | toolbox/python/nltk.ipynb | martinapugliese/tales-science-data-notebooks |
Playing with frequency distributions | # Setting some sentences
sentences = ['I go to school', 'I will go to the cinema', 'I like strawberries', 'I read books']
# FreqDist on the word length on some chosen sentences and on the last letter of words
split_sentences = [sentence.split() for sentence in sentences]
all_words = []
for sent in split_sentences:
... | _____no_output_____ | MIT | toolbox/python/nltk.ipynb | martinapugliese/tales-science-data-notebooks |
SLU15 - Hyperparameter tunning: Examples notebook--- 1 Load and the prepare the data | import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
cancer_data = load_breast_cancer()
X = pd.DataFrame(cancer_data["data"], columns=cancer_data["feature_names"])
y = cancer_data.target
X_train, X_test... | _____no_output_____ | MIT | S01 - Bootcamp and Binary Classification/SLU15 - Hyperparameter Tuning/Examples notebook.ipynb | FarhadManiCodes/batch5-students |
2 Grid search | from sklearn.model_selection import GridSearchCV
parameters = {'max_depth': range(1, 10),
'max_features': range(1, X.shape[1])}
grid_search = GridSearchCV(estimator, parameters, cv=5, scoring="roc_auc")
grid_search.fit(X_train, y_train)
y_pred = grid_search.predict(X_test) | _____no_output_____ | MIT | S01 - Bootcamp and Binary Classification/SLU15 - Hyperparameter Tuning/Examples notebook.ipynb | FarhadManiCodes/batch5-students |
2 Random search | from scipy.stats import randint
from sklearn.model_selection import RandomizedSearchCV
parameters_dist = {"max_depth": randint(1, 100),
"max_features": randint(1, X.shape[1]),
"class_weight": ["balanced", None]}
random_search = RandomizedSearchCV(estimator, parameters_dist, cv=5,... | _____no_output_____ | MIT | S01 - Bootcamp and Binary Classification/SLU15 - Hyperparameter Tuning/Examples notebook.ipynb | FarhadManiCodes/batch5-students |
The analysis of the equality of rights between gender using the Human Freedom Index author: Ottillia Ni Project Report 2 (EM212: Applied Data Science) Content:IntroductionDatasheetExploratory Data Anaylsis Introduction Throughout the world, people strive for freedom. Freedom is a means of human progression and grow... | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import pdb
import matplotlib.pyplot as plt | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
Importing DatasetTo begin, I will be using python to analyize my data. (This data of the Human Freedom Index is downloaded off Kaggle.) | # read Human Freedom Index data
hfi = pd.read_csv('https://tufts.box.com/shared/static/7iwsgxhffhfs87v209scqihq57pnmev0.csv')
hfi.head() | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
Cleaning Data Given that I am focusing specifically on the equality between female and male, I want to clean my data so that the variables printed give me the information relavent to my work. In addition, to make understanding the data easier, I will also rename various columns to be more comprehensive of what each v... | select_cols = ["year","countries","region","pf_ss_women","pf_ss_women_fgm", "pf_ss_women_missing","pf_ss_women_inheritance","pf_ss_women_inheritance_widows","pf_ss_women_inheritance_daughters","pf_movement_women","pf_identity_legal","pf_identity_parental","pf_identity_parental_marriage","pf_identity_parental_divorce","... | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
Printing the data types objects makes it immediately evident that the data is mostly in forms of numbers, thus indicating much of the data has already been cleaned. | #Let us determine the percent of data missing per variable.
#f, ax = plt.subplots(figsize=(50,20))
#((hfi.isnull().sum()/len(hfi)) * 100).plot(kind='bar')
#plt.xticks(rotation=45, horizontalalignment='right')
#plt.title('Percent Missing by Variable')
# a simple scatterplot
#hfi.plot.scatter('pf_score', 'ef_score')
#h... | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
To start off we can first lay out the number of countries represented by region through this bar plot from 2008-2016. | #hfi.region.value_counts().plot(kind='bar')
#plt.xticks()
#hfi[''].value_counts().plot(kind='bar')
#filter to only focus on 2016 data
filter1 = hfi_select1.year == 2016
hfi2016 = hfi_select1[filter1]
hfi2016.sample(5)
#filter to only focuus on 2016 data
filter1 = hfi_select1.year == 2016
filter2 = hfi_select1.region =... | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
Diving into gender equality, let us understand observe the equality of parental rights between males and females in various regions around the world. | #hfi2016Af.plot.scatter('pf_ss_women', 'pf_movement_women')
#sub['mean'] = sub['density'].mean()
#plt.plot(sub['name'], sub['density'], 'ro')
#plt.plot(sub['name'], sub['mean'], linestyle = '--')
#plt.xticks(fontsize = 8, rotation = 'vertical')
hfi2016.region.value_counts().plot(kind='bar')
plt.xticks()
sns.heatmap(hf... | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
According to the Human Freedom Report, "Parental rights refers to the extent to which women have equal rights based in law and custom regarding “legal guardianship of a child during a marriage and custody rights over a child after divorce.”" That being said, we can divide the rights parental rights in regards to legal ... | sns.heatmap(hfi_select1.groupby(['year', 'region'])['Parental_rights_marriage'].mean().unstack(),
annot=True, cbar=False, fmt='.0f', cmap='RdBu_r') | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
The same analysis can also be done with parental rights after divorces. The trend appears to be similar to that of parental rights during marriages, but what is most surprising is found in the Middle East & North Africa catagory. The ranking drops from a 5 to a 2 between 2012 and 2013 indicating a setback with progre... | sns.heatmap(hfi_select1.groupby(['year', 'region'])['Parental_rights_after_divorce'].mean().unstack(),
annot=True, cbar=False, fmt='.0f', cmap='RdBu_r') | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
Question: Which regions have the greatest amount of freedom in regards to same sex marriage? | sns.heatmap( hfi_select1.groupby(['year', 'region'])['Same_sex-relationship'].mean().unstack(),
annot=True, cbar=False, fmt='.0f', cmap='RdBu_r') | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
From this heat map, we can see that the regions struggle with equality | sns.heatmap(hfi_select1.groupby(['year', 'region'])['Same_sex_males'].mean().unstack(),
annot=True, cbar=False, fmt='.0f', cmap='RdBu_r')
sns.heatmap(hfi_select1.groupby(['year', 'region'])['Same_sex_female'].mean().unstack(),
annot=True, cbar=False, fmt='.0f', cmap='RdBu_r') | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
Merging Data | # read women purchasing power from https://ourworldindata.org/economic-inequality-by-gender
ge = pd.read_csv('https://tufts.box.com/shared/static/ikc9nsb0red47dv5ldc0rcsv5rml681l.csv')
ge.head()
#ge.dtypes
mergedata = hfi_select1.merge(ge, left_on=["year", "countries"], right_on=["Year", "Entity"], suffixes=(False, Fal... | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
By merging these two datasets, we can also compare how women in various countries are given the opportunity to participate in purchase descision within their marriages. | #mergedata.dtypes | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
supervised learning - Scikit.learn | #hfi2016.plot.scatter('Inheritance_Rights', 'Parental_rights')
f, ax = plt.subplots(figsize=(6.5,6.5))
sns.boxplot(x="Inheritance_Rights", y="Parental_rights", data=hfi2016, fliersize=0.5, linewidth=0.75, ax=ax)
#ax.set_title('axes title')
ax.set_xlabel('Women Inheritance')
ax.set_ylabel('Parental Rights')
#filter to ... | _____no_output_____ | BSD-3-Clause | ottilliani/project2_ottilliani.ipynb | pezLyfe/applied_ds |
第5章 ロジスティック回帰とROC曲線：学習モデルの評価方法 「05-roc_curve」の解説 ITエンジニアための機械学習理論入門「第5章 ロジスティック回帰とROC曲線:学習モデルの評価方法」で使用しているサンプルコード「05-roc_curve.py」の解説です。※ 解説用にコードの内容は少し変更しています。 はじめに必要なモジュールをインポートしておきます。関数 multivariate_normal は、多次元の正規分布に従う乱数を生成するために利用します。 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import Series, DataFrame
from numpy.random import multivariate_normal | _____no_output_____ | Apache-2.0 | 05-roc_curve.ipynb | RXV06021/test_ml4se_colab |
トレーニング用データを生成する関数を用意します。平面上の○☓の2種類のデータについて、それぞれの「個数、中心座標、分散」を引数で指定します。 | def prepare_dataset(n1, mu1, variance1, n2, mu2, variance2):
df1 = DataFrame(multivariate_normal(mu1, np.eye(2)*variance1 ,n1),
columns=['x','y'])
df1['type'] = 1
df2 = DataFrame(multivariate_normal(mu2, np.eye(2)*variance2, n2),
columns=['x','y'])
df2['type'] = -... | _____no_output_____ | Apache-2.0 | 05-roc_curve.ipynb | RXV06021/test_ml4se_colab |
ロジスティック回帰で分割線を決定する関数を用意します。ここでは、得られた結果を用いて、トレーニングセットの各データに対して確率の値を付与したデータフレームも返却するようにしています。 | # ロジスティック回帰
def run_logistic(train_set):
pd.options.mode.chained_assignment = None
w = np.array([[0],[0.1],[0.1]])
phi = train_set[['x','y']]
phi['bias'] = 1
phi = phi.as_matrix(columns=['bias','x','y'])
t = (train_set[['type']] + 1)*0.5 # type = 1, -1 を type = 1, 0 に変換
t = t.as_matrix()
... | _____no_output_____ | Apache-2.0 | 05-roc_curve.ipynb | RXV06021/test_ml4se_colab |
結果をグラフ、および、ROC曲線として表示する関数を用意します。 | # 結果の表示
def show_result(subplot, train_set, w0, w1, w2, err_rate):
train_set1 = train_set[train_set['type']==1]
train_set2 = train_set[train_set['type']==-1]
ymin, ymax = train_set.y.min()-5, train_set.y.max()+10
xmin, xmax = train_set.x.min()-5, train_set.x.max()+10
subplot.set_ylim([ymin-1, ymax+... | _____no_output_____ | Apache-2.0 | 05-roc_curve.ipynb | RXV06021/test_ml4se_colab |
比較的分散が小さくて、分類が容易なトレーニングセットを用意します。 | train_set = prepare_dataset(80, [9,9], 50, 200, [-3,-3], 50) | _____no_output_____ | Apache-2.0 | 05-roc_curve.ipynb | RXV06021/test_ml4se_colab |
ロジスティック回帰を適用した結果を表示します。 | w0, w1, w2, err_rate, result = run_logistic(train_set)
fig = plt.figure(figsize=(6, 12))
subplot = fig.add_subplot(2,1,1)
show_result(subplot, train_set, w0, w1, w2, err_rate)
subplot = fig.add_subplot(2,1,2)
draw_roc(subplot, result) | _____no_output_____ | Apache-2.0 | 05-roc_curve.ipynb | RXV06021/test_ml4se_colab |
分散が大きくて、分類が困難なトレーニングセットを用意します。 | train_set = prepare_dataset(80, [9,9], 150, 200, [-3,-3], 150) | _____no_output_____ | Apache-2.0 | 05-roc_curve.ipynb | RXV06021/test_ml4se_colab |
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