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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> **Quantas vezes um produto foi parcelado** | #juntar tabela items(product_id) com a tabela payments(payment_installments) em order_id
most_products_installments = pd.merge(left=items, right= payments, on='order_id')
products_by_installments = most_products_installments.groupby(['product_id','payment_installments'])['payment_installments'].size()
products_by_insta... | _____no_output_____ | MIT | DataScience/Olist/EnfaseLabs.ipynb | brunereduardo/DataPortfolio |
>**ticket médio = Soma do faturamento em vendas (R$)/Nº de vendas concluídas** Análise para Logística: >Maior valor de frete encontrado por estado | aux = pd.merge(left=items, right=orders, on= 'order_id') #customers(ligando pelo customer_id e pegando os valores de estado e cidade)
# items(order_id e pegando freight_value) #orders(ligando por order_id e pegando o customer_id)
biggest_freight_value = pd.merge(left=customers, right=aux, on='customer_id')
freight_valu... | _____no_output_____ | MIT | DataScience/Olist/EnfaseLabs.ipynb | brunereduardo/DataPortfolio |
debug Class> API details. | #hide
import logging
logging.basicConfig(level= logging.WARNING)
log = logging.getLogger("pynamodb")
log.setLevel(logging.DEBUG)
log.setLevel(logging.WARNING)
log.propagate = True
#hide
import pickle, os, json
os.environ['DATABASE_TABLE_NAME'] = 'product-table-dev-manual'
os.environ['BRANCH'] = 'dev'
os.environ['REGI... | _____no_output_____ | Apache-2.0 | debug.ipynb | thanakijwanavit/villa-product-database |
LambdaDumpOnline | lambdaDumpOnlineS3({})
from inspect import getsource
print(getsource(lambdaDumpOnlineS3)) | def lambdaDumpOnlineS3(event, *args):
print(f'ecommece col list is {ECOMMERCE_COL_LIST}')
# get all products from db
df:pd.DataFrame = pd.DataFrame([i.data for i in ProductDatabase.scan()])
# get online list from ECOMMERCE_COL_LIST
onlineList:List[str] = yaml.load(requests.get(ECOMMERCE_COL_LIST).content)
#... | Apache-2.0 | debug.ipynb | thanakijwanavit/villa-product-database |
get all products | df:pd.DataFrame = pd.DataFrame([i.data for i in ProductDatabase.scan()])
df.head() | _____no_output_____ | Apache-2.0 | debug.ipynb | thanakijwanavit/villa-product-database |
filter products | condition1 = df['master_online'] == True
condition2 = df['hema_name_en'] != ''
onlineList:List[str] = yaml.load(requests.get(ECOMMERCE_COL_LIST).content)
onlineDf:pd.DataFrame = df[condition1 & condition2].loc[:,onlineList]
onlineDf.head()
df.shape
df[condition1].shape ## filter for master_online
df[condition1 & condit... | _____no_output_____ | Apache-2.0 | debug.ipynb | thanakijwanavit/villa-product-database |
**Check whether two string are anagram of each other** :
w1=w1.replace(' ','').lower()
w2=w2.replace(' ','').lower()
return sorted(w1)==sorted(w2)
anagram('do g','GOd')
def anagramer(w1,w2):
w1=w1.replace(' ','').lower()
w2=w2.replace(' ','').lower()
if len(w1) != len(w2):
return False
count ={}
for letter in w1:
print(l... | _____no_output_____ | MIT | 001_Anagram_Check.ipynb | ishancoderr/Coffee_code_Algorithms |
MPST: A Corpus of Movie Plot Synopses with Tags | !pip install scikit-multilearn
import re
import os
import tqdm
import nltk
import pickle
import sqlite3
import warnings
import numpy as np
import pandas as pd
from tqdm import tqdm
import seaborn as sns
import xgboost as xgb
import tensorflow as tf
from sklearn import metrics
from tensorflow import keras
from nltk.cor... | _____no_output_____ | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
1. TfidfVectorizer with 1 grams: | tf_vectorizer = TfidfVectorizer(min_df=0.09, tokenizer = lambda x: x.split(" "), ngram_range=(1,1))
X_train_multilabel = tf_vectorizer.fit_transform(X_train)
X_test_multilabel = tf_vectorizer.transform(X_test)
print("Dimensions of train data X:",X_train_multilabel.shape, "Y :",y_train_multilabel.shape)
print("Dimensi... | Dimensions of train data X: (10989, 657) Y : (10989, 3)
Dimensions of test data X: (2768, 657) Y: (2768, 3)
| Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
1.1 OneVsRestClassifier + MultinomialNB: | mb = MultinomialNB(class_prior = [0.5, 0.5])
clf = OneVsRestClassifier(mb)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction1 = clf.predict(X_test_multilabel)
precision1 = precision_score(y_test_multilabel, prediction1, average='micro')
recall1 = recall_score(y_test_multilabel, prediction1, average='micro')... | Movie: Anastasia
Actual genre: romantic, cute, entertaining
Predicted tag: []
Movie: Dog City
Actual genre: psychedelic
Predicted tag: []
Movie: Aftermath
Actual genre: violence
Predicted tag: ['flashback' 'murder' 'violence']
Movie: What a Nightmare, Charlie Brown!
Actual genre: psychedelic
Predicted ... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
1.2 OneVsRestClassifier + SGDClassifier with LOG Loss: | sgl = SGDClassifier(loss='log', class_weight='balanced')
clf = OneVsRestClassifier(sgl)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction2 = clf.predict(X_test_multilabel)
precision2 = precision_score(y_test_multilabel, prediction2, average='micro')
recall2 = recall_score(y_test_multilabel, prediction2, ave... | Movie: Capitalism: A Love Story
Actual genre: sentimental
Predicted tag: ['flashback']
Movie: Saved!
Actual genre: romantic, humor, satire
Predicted tag: []
Movie: How to Train Your Dragon 2
Actual genre: revenge, cute
Predicted tag: ['violence']
Movie: Orfeu Negro
Actual genre: atmospheric
Predicted t... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
1.3 OneVsRestClassifier + SGDClassifier with Hinge Loss: | sgh = SGDClassifier(loss='hinge', class_weight='balanced')
clf = OneVsRestClassifier(sgh)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction3 = clf.predict(X_test_multilabel)
precision3 = precision_score(y_test_multilabel, prediction3, average='micro')
recall3 = recall_score(y_test_multilabel, prediction3, a... | Movie: Assassin's Creed IV: Black Flag
Actual genre: action
Predicted tag: ['flashback' 'murder' 'violence']
Movie: The Kite Runner
Actual genre: romantic, violence, murder, storytelling, flashback
Predicted tag: ['flashback' 'violence']
Movie: Guest House Paradiso
Actual genre: psychedelic, comedy
Predicte... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
1.4 OneVsRestClassifier + LogisticRegression: | lr = LogisticRegression(class_weight='balanced')
clf = OneVsRestClassifier(lr)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction4 = clf.predict(X_test_multilabel)
precision4 = precision_score(y_test_multilabel, prediction4, average='micro')
recall4 = recall_score(y_test_multilabel, prediction4, average='mic... | Movie: The Sting
Actual genre: boring, depressing, murder, cult, plot twist, clever, inspiring, revenge, entertaining
Predicted tag: ['flashback' 'murder']
Movie: Olivia
Actual genre: neo noir, cruelty, murder, violence, revenge, sadist
Predicted tag: []
Movie: El capo
Actual genre: satire
Predicted tag: [... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
2. TfidfVectorizer with (1 - 2 Grams): | tf_vectorizer = TfidfVectorizer(min_df=0.09, tokenizer = lambda x: x.split(" "), ngram_range=(1,2))
X_train_multilabel = tf_vectorizer.fit_transform(X_train)
X_test_multilabel = tf_vectorizer.transform(X_test)
print("Dimensions of train data X:",X_train_multilabel.shape, "Y :",y_train_multilabel.shape)
print("Dimensi... | Dimensions of train data X: (10989, 666) Y : (10989, 3)
Dimensions of test data X: (2768, 666) Y: (2768, 3)
| Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
2.1 OneVsRestClassifier + MultinomialNB : | mb = MultinomialNB(class_prior = [0.5, 0.5])
clf = OneVsRestClassifier(mb)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction5 = clf.predict(X_test_multilabel)
precision5 = precision_score(y_test_multilabel, prediction5, average='micro')
recall5 = recall_score(y_test_multilabel, prediction5, average='micro')... | Movie: Long-Distance Princess
Actual genre: romantic
Predicted tag: ['flashback']
Movie: Time Lapse
Actual genre: plot twist, mystery, murder
Predicted tag: ['murder']
Movie: What's Eating Gilbert Grape
Actual genre: tragedy, whimsical, psychedelic, boring, depressing
Predicted tag: ['flashback']
Movie: ... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
2.2 OneVsRestClassifier + SGDClassifier with LOG Loss : | sgl = SGDClassifier(loss='log', class_weight='balanced')
clf = OneVsRestClassifier(sgl)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction6 = clf.predict(X_test_multilabel)
precision6 = precision_score(y_test_multilabel, prediction6, average='micro')
recall6 = recall_score(y_test_multilabel, prediction6, ave... | Movie: Cape Fear
Actual genre: suspenseful, gothic, murder, neo noir, mystery, violence, cult, horror, flashback, good versus evil, revenge
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Wild Bill
Actual genre: cult, revenge, storytelling, flashback
Predicted tag: ['violence']
Movie: Beyond the Law
... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
2.3 OneVsRestClassifier + SGDClassifier with HINGE Loss : | sgh = SGDClassifier(loss='hinge', class_weight='balanced')
clf = OneVsRestClassifier(sgh)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction7 = clf.predict(X_test_multilabel)
precision7 = precision_score(y_test_multilabel, prediction7, average='micro')
recall7 = recall_score(y_test_multilabel, prediction7, a... | Movie: Les deux orphelines vampires
Actual genre: insanity
Predicted tag: ['murder']
Movie: The Blob
Actual genre: violence, cult, suspenseful, comedy, murder
Predicted tag: ['murder']
Movie: La casa muda
Actual genre: plot twist, revenge
Predicted tag: []
Movie: Fist Fight
Actual genre: prank
Predicte... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
2.4 OneVsRestClassifier + LogisticRegression: | lr = LogisticRegression(class_weight='balanced')
clf = OneVsRestClassifier(lr)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction8 = clf.predict(X_test_multilabel)
precision8 = precision_score(y_test_multilabel, prediction8, average='micro')
recall8 = recall_score(y_test_multilabel, prediction8, average='mic... | Movie: Hare and Loathing in Las Vegas
Actual genre: psychedelic
Predicted tag: []
Movie: Misconduct
Actual genre: neo noir
Predicted tag: ['murder' 'violence']
Movie: Ten Little Indians
Actual genre: murder
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Hostel
Actual genre: boring, gothic, mur... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
3. TfidfVectorizer with (1 - 3 Grams): | tf_vectorizer = TfidfVectorizer(min_df=0.09, tokenizer = lambda x: x.split(" "), ngram_range=(1,3))
X_train_multilabel = tf_vectorizer.fit_transform(X_train)
X_test_multilabel = tf_vectorizer.transform(X_test)
print("Dimensions of train data X:",X_train_multilabel.shape, "Y :",y_train_multilabel.shape)
print("Dimensi... | Dimensions of train data X: (10989, 666) Y : (10989, 3)
Dimensions of test data X: (2768, 666) Y: (2768, 3)
| Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
3.1 OneVsRestClassifier + MultinomialNB : | mb = MultinomialNB(class_prior = [0.5, 0.5])
clf = OneVsRestClassifier(mb)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction9 = clf.predict(X_test_multilabel)
precision9 = precision_score(y_test_multilabel, prediction9, average='micro')
recall9 = recall_score(y_test_multilabel, prediction9, average='micro')... | Movie: Tezaab
Actual genre: revenge, romantic, flashback
Predicted tag: ['flashback' 'murder' 'violence']
Movie: My Summer of Love
Actual genre: dramatic, romantic, queer
Predicted tag: ['flashback']
Movie: The Ten Commandments: The Musical
Actual genre: historical fiction
Predicted tag: ['violence']
Mov... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
3.2 OneVsRestClassifier + SGDClassifier with LOG Loss : | sgl = SGDClassifier(loss='log', class_weight='balanced')
clf = OneVsRestClassifier(sgl)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction10 = clf.predict(X_test_multilabel)
precision10 = precision_score(y_test_multilabel, prediction10, average='micro')
recall10 = recall_score(y_test_multilabel, prediction10... | Movie: American Son
Actual genre: violence, romantic, flashback
Predicted tag: ['flashback']
Movie: Conan the Destroyer
Actual genre: murder, cult, fantasy, alternate history, violence
Predicted tag: ['murder' 'violence']
Movie: Love & Other Drugs
Actual genre: romantic
Predicted tag: ['flashback']
Movie... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
3.3 OneVsRestClassifier + SGDClassifier with HINGE Loss : | sgh = SGDClassifier(loss='hinge', class_weight='balanced')
clf = OneVsRestClassifier(sgh)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction11 = clf.predict(X_test_multilabel)
precision11 = precision_score(y_test_multilabel, prediction11, average='micro')
recall11 = recall_score(y_test_multilabel, prediction... | Movie: Austin Powers: International Man of Mystery
Actual genre: comedy, boring, cult, good versus evil, psychedelic, humor, satire, revenge, entertaining
Predicted tag: []
Movie: American Dreamer
Actual genre: intrigue
Predicted tag: ['flashback']
Movie: Shootout at Wadala
Actual genre: violence
Predicted ... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
3.4 OneVsRestClassifier + LogisticRegression: | lr = LogisticRegression(class_weight='balanced')
clf = OneVsRestClassifier(lr)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction12 = clf.predict(X_test_multilabel)
precision12 = precision_score(y_test_multilabel, prediction12, average='micro')
recall12 = recall_score(y_test_multilabel, prediction12, average... | Movie: End of Days
Actual genre: mystery, neo noir, murder, stupid, violence, flashback, good versus evil, suspenseful
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Zombeavers
Actual genre: absurd, entertaining
Predicted tag: ['violence']
Movie: The Witches of Eastwick
Actual genre: comedy
Predict... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
4. TfidfVectorizer with (1 - 4 Grams): | tf_vectorizer = TfidfVectorizer(min_df=0.09, tokenizer = lambda x: x.split(" "), ngram_range=(1, 4))
X_train_multilabel = tf_vectorizer.fit_transform(X_train)
X_test_multilabel = tf_vectorizer.transform(X_test)
print("Dimensions of train data X:",X_train_multilabel.shape, "Y :",y_train_multilabel.shape)
print("Dimens... | Dimensions of train data X: (10989, 666) Y : (10989, 3)
Dimensions of test data X: (2768, 666) Y: (2768, 3)
| Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
4.1 OneVsRestClassifier + MultinomialNB : | mb = MultinomialNB(class_prior = [0.5, 0.5])
clf = OneVsRestClassifier(mb)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction13 = clf.predict(X_test_multilabel)
precision13 = precision_score(y_test_multilabel, prediction13, average='micro')
recall13 = recall_score(y_test_multilabel, prediction13, average='mi... | Movie: Hold Your Breath
Actual genre: violence
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Nae Yeojachinguneun Gumiho
Actual genre: romantic
Predicted tag: []
Movie: World Without End
Actual genre: murder
Predicted tag: ['violence']
Movie: Absolon
Actual genre: philosophical
Predicted tag: ... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
4.2 OneVsRestClassifier + SGDClassifier with LOG Loss : | sgl = SGDClassifier(loss='log', class_weight='balanced')
clf = OneVsRestClassifier(sgl)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction14 = clf.predict(X_test_multilabel)
precision14 = precision_score(y_test_multilabel, prediction14, average='micro')
recall14 = recall_score(y_test_multilabel, prediction14... | Movie: The Spanish Main
Actual genre: intrigue, action, violence
Predicted tag: ['violence']
Movie: The Importance of Being Earnest
Actual genre: romantic
Predicted tag: []
Movie: Anastasia
Actual genre: romantic, cute, entertaining
Predicted tag: ['flashback']
Movie: Flawless
Actual genre: violence, r... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
4.3 OneVsRestClassifier + SGDClassifier with HINGE Loss : | sgh = SGDClassifier(loss='hinge', class_weight='balanced')
clf = OneVsRestClassifier(sgh)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction15 = clf.predict(X_test_multilabel)
precision15 = precision_score(y_test_multilabel, prediction15, average='micro')
recall15 = recall_score(y_test_multilabel, prediction... | Movie: Elizabeth: The Golden Age
Actual genre: intrigue
Predicted tag: ['murder']
Movie: Dishonored 2
Actual genre: sci-fi
Predicted tag: ['murder' 'violence']
Movie: In Your Eyes
Actual genre: paranormal, romantic, fantasy, mystery
Predicted tag: []
Movie: Final Fantasy: The Spirits Within
Actual genre... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
4.4 OneVsRestClassifier + LogisticRegression: | lr = LogisticRegression(class_weight='balanced')
clf = OneVsRestClassifier(lr)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction16 = clf.predict(X_test_multilabel)
precision16 = precision_score(y_test_multilabel, prediction16, average='micro')
recall16 = recall_score(y_test_multilabel, prediction16, average... | Movie: Red Sky
Actual genre: violence, murder
Predicted tag: []
Movie: Last Summer
Actual genre: violence, cruelty, romantic
Predicted tag: []
Movie: Real Life
Actual genre: satire
Predicted tag: []
Movie: One Crazy Summer
Actual genre: psychedelic
Predicted tag: []
Movie: Mumsy, Nanny, Sonny & Gir... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
Conclusion: | from prettytable import PrettyTable
tabel = PrettyTable()
tabel.field_names=['Model','Vectorizer','ngrams','Precision','recall','f1_score']
tabel.add_row(['MultinomialNB', 'TfidfVectorizer', '(1, 1)', round(precision1, 3),round(recall1, 3), round(f1_score1, 3)])
tabel.add_row(['SGDClassifier(log)', 'TfidfVectoriz... | +----------------------+-----------------+--------+-----------+--------+----------+
| Model | Vectorizer | ngrams | Precision | recall | f1_score |
+----------------------+-----------------+--------+-----------+--------+----------+
| MultinomialNB | TfidfVectorizer | (1, 1) | 0.467 | 0.68... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
5. Word2Vec | X_train_new = []
for i in tqdm(range(len(list(X_train)))):
X_train_new.append(X_train[i].split(" "))
with open('glove.6B.300d.pkl', 'rb') as f:
new_model = pickle.load(f)
words = set(new_model.keys())
X_train_multilabel = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tq... | 100%|█████████████████████████████████████████████████████████████████████████████| 2768/2768 [00:03<00:00, 877.01it/s]
| Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
5.1 OneVsRestClassifier + SGDClassifier with LOG Loss : | sgl = SGDClassifier(loss='log', class_weight='balanced')
clf = OneVsRestClassifier(sgl)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction17 = clf.predict(X_test_multilabel)
precision17 = precision_score(y_test_multilabel, prediction17, average='micro')
recall17 = recall_score(y_test_multilabel, prediction17... | Movie: Topper
Actual genre: comedy
Predicted tag: []
Movie: La otra
Actual genre: murder, melodrama
Predicted tag: ['flashback' 'murder']
Movie: Conan the Destroyer
Actual genre: murder, cult, fantasy, alternate history, violence
Predicted tag: ['violence']
Movie: Magnificent Obsession
Actual genre: me... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
5.2 OneVsRestClassifier + SGDClassifier with HINGE Loss : | sgh = SGDClassifier(loss='hinge', class_weight='balanced')
clf = OneVsRestClassifier(sgh)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction18 = clf.predict(X_test_multilabel)
precision18 = precision_score(y_test_multilabel, prediction18, average='micro')
recall18 = recall_score(y_test_multilabel, prediction... | Movie: Orange County
Actual genre: flashback
Predicted tag: []
Movie: Mo' Money
Actual genre: murder
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Serbuan maut
Actual genre: cult, suspenseful, murder, violence, flashback
Predicted tag: ['murder' 'violence']
Movie: Dog Park
Actual genre: roman... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
5.3 OneVsRestClassifier + LogisticRegression: | lr = LogisticRegression(class_weight='balanced')
clf = OneVsRestClassifier(lr)
clf.fit(X_train_multilabel, y_train_multilabel)
prediction19 = clf.predict(X_test_multilabel)
precision19 = precision_score(y_test_multilabel, prediction19, average='micro')
recall19 = recall_score(y_test_multilabel, prediction19, average... | Movie: In the Line of Fire
Actual genre: suspenseful, neo noir, murder, mystery, violence, revenge, flashback, good versus evil, humor, action, romantic
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Huang jia shi jie
Actual genre: violence
Predicted tag: ['murder']
Movie: The Mirror Crack'd
Actual ... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
Conclusion | from prettytable import PrettyTable
tabel = PrettyTable()
tabel.field_names=['Model', 'Vectorizer', 'Precision','recall','f1_score']
tabel.add_row(['SGDClassifier(log)', 'AVG W2V', round(precision17, 3), round(recall17, 3), round(f1_score17, 3)])
tabel.add_row(['SGDClassifier(hinge)','AVG W2V', round(precision18,... | +----------------------+------------+-----------+--------+----------+
| Model | Vectorizer | Precision | recall | f1_score |
+----------------------+------------+-----------+--------+----------+
| SGDClassifier(log) | AVG W2V | 0.423 | 0.788 | 0.551 |
| SGDClassifier(hinge) | AVG W2V | ... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
6. LSTM-CNN Model | max_review_length = 400
X_train = sequence.pad_sequences(X_train_multilabel, maxlen=max_review_length, padding='post')
X_test = sequence.pad_sequences(X_test_multilabel, maxlen=max_review_length, padding='post')
inputt = 8252
batch_size = 32
epochs = 10
model =Sequential()
model.add(Embedding(inputt, 50, input_length=... | Movie: Beyond Tomorrow
Actual genre: romantic, murder
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Rabbit's Kin
Actual genre: psychedelic
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Zerophilia
Actual genre: pornographic
Predicted tag: ['flashback' 'murder' 'violence']
Movie: Austi... | Apache-2.0 | IPYNB Notebooks/modeling_with_top_3_tags.ipynb | sandeeppanda22/Movie-Tag-Prediction- |
Jupyter Notebook: Prioritization of Syphilis Serologies for Investigation (Modernizing the Syphilis Reactor Grid) Notes to keep in mind when using this notebook:* Please ignore the number on the left (that says "In [x]") when running these cells. When we refer to a cell number, look for it in the beginning of the cel... | # Cell number 1
import sys
import pandas as pd
import numpy as np
import os
import textwrap
import datetime as dt
from collections import defaultdict
# warning suppression
import warnings
warnings.filterwarnings('ignore') | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 2: Jupyter Notebook cells output only 1 result by default. Lines 3-4 enable the cell to output all the results as an outcome of the script in the cell. This cell also holds several utility functions for the main algorithm loop. We've also included a convenience function to convert XLSX (excel) files to CSV... | # Cell number 2
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
### Utility Functions
# cleans a column of strings by typecasting to str and stripping whitespace
def cleanstring(df, column):
df[column] = df[column].astype(str) # converts column items int... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 3: This is the script to import and read the CSV file. Please copy/paste the address of your file in place of "Please enter your file path and file name here.csv". This file is read into a DataFrame: `df_main`. It may take a while for the data to be read into `df_main`, so please wait for the cell to be f... | #Cell number 3
df_main = pd.read_csv("Please enter your file path and file name here.csv") | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 4: Displays the column names of the imported file ( saved as `df_main`) and the first 5 lines of your uploaded dataframe | #Cell number 4
df_main_columns = df_main.columns
df_main.columns = [x.strip() for x in df_main.columns]
df_main.columns
df_main.head() | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 5: This changes the column names for the dataframe. The DataFrame might contain more elements than is required for this algorithm. Those column names can be left as is. Please change the corresponding column names for the following data elements to:| Column Name | Data Element || --- | --- || `ID_Profile` ... | #Cell number 5
df_main.columns
df_main.rename(columns={'DS_QualitativeResult':'QualitativeResult', 'DS_QuantitativeResult':'QuantitativeResult', 'DS_Test':'Test', 'DS_Disposition':'Disposition'}, inplace=True)
df_main.columns | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 6: The CSV file does not have date attributes. The script below will assign a python date attribute to this column. The same script can be used for any other date elements. **The CSV file must contain date the date in the YYYY-MM-DD format. If it is not possible to create it in this format, please let us ... | #Cell number 6
# function to convert strings in column `var` in pandas DataFrame `df` to datetime
def str2date(df, var, dateformat='%Y-%m-%d'):
cleanstring(df, var)
# The line below can be adjusted to meet unique formats. For example, if the date appears as MM/DD/YYYY, the following
# format will wor... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 7: The database might default titers as '1:x', the following code removes the '1:' part from it and converts it to an integer. Only use the script below if the quantitative result in the dataframe is not extracted as a number. The script below takes away "1:" from, converts it into a number, and converts n... | #Cell number 7
def cleannum(a):
if a == 'nan':
return 0
else:
return (a[2:])
def cleanquant(df, var):
cleanstring(df, var)
df['quanttest'] = df[var].apply(cleannum)
df['quanttest'] = df['quanttest'].astype(int)
# cell number 7.b
df_main['QuantitativeResult'].value_counts(... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 8: If the quantitative result or titer was a number in the data extract and cell number 7 was not required, we change the column name to `quanttest` Run Cell number 8 customized to the column headings, labelling the column with titer results (quantitative results) as quanttest, only if cell number 7 was no... | #Cell number 8
#df_main.columns = ['columnA', 'ID_Profile', 'columnB', 'DS_QualitativeResult','DT_Specimen', 'columnC', 'quanttest', 'DS_Test', 'columnD','CD_Gender','Age_clean']
| _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 9: As part of data-wrangling, the following script ensures that the values for `ID_Profile`, `QualitativeResult`, and `Test` do not contain any spaces and are in string format. The data type displayed should show :| Column Name | Datatype || --- | --- || `ID_Profile` | `object` || `Test` | `object` || `DT_... | #Cell number 9
dirty_columns = ['ID_Profile', 'QualitativeResult', 'Test', 'CD_Gender', 'Disposition']
for c in dirty_columns:
cleanstring(df_main, c)
df_main.dtypes | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 10: Removes all tests with no dates (they can't be calculated in the algorithm) and reveals how many rows and columns are there in the DataFrame. The following step is not compulsory, but it ensures that all specimen dates have dates else the program will crash. | # Cell number 10
nulldates = df_main[df_main['DT_Specimen'].isnull()]
beforelen = len(np.unique(nulldates['ID_Profile']))
nulldates_idx = nulldates.index
df_main = df_main.drop(nulldates_idx)
afterlen = len(np.unique(df_main['ID_Profile']))
# ------ output ------
print(f"")
print(f"Number of Null Dates Before Remova... |
Number of Null Dates Before Removal: 7946
Shape: (534599, 24)
Number of Tests After Null Removal: 113507
| Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 11: Creating a DataFrame for running the algorithm with only the essential columns. Since we select the core data elements, we remove duplicates (same person, same test, same result, same day). The row index is created as a separate column. This `index_no` will be used to join the rest of the data elements... | # Cell number 11
df_main_dd = df_main[['ID_Profile','Test','DT_Specimen','quanttest','QualitativeResult','DT_Disposition']]
df_main_dd = df_main_dd.drop_duplicates()
df_main_dd['index_no'] = df_main_dd.index
df_main_dd = df_main_dd.merge(df_main, left_on = 'index_no', right_on = 'S_No', how = 'left')
# df_main_dd dr... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 12: This is were we define all of our variables. This ensures that we don't have to modify the codes everytime and can just make some adjustments here | # Cell number 12
#Step 8 allows for a cut-off date for titers since we found that titers can be from a long time ago and should not be compared.
#This can be modified anytime. We define the duration as 'DaysBetweenTests'
DaysBetweenTests = 365
#Step 8 quantifies what should be considered a high titer, given that the... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Algorithm to Prioritize Reactive Non-Treponemal Tests Reported to Health Departments for Investigating Suspected Cases of Syphilis Step 1: select only reactive Non-Treponemal Tests (NTT) Cell number 13 selects the tests for Step 1 in the algorithm | # cell number 13
dfm_6m_ntt = df_main_dd[(df_main_dd['Test'] == RPR) | (df_main_dd['Test'] == VDRL) | (df_main_dd['Test'] == CSF_VDRL)| (df_main_dd['Test'] == RPR_CordBlood)|(df_main_dd['Test']==TRUST)]
dfm_6m_ntt = dfm_6m_ntt[(dfm_6m_ntt['QualitativeResult'] == R) | (dfm_6m_ntt['QualitativeResult'] == W) | (dfm_6m_ntt... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 13.1. We want to identify the first test in the latest DT_Disposition (episode of disease). Ideally, we would want to assign a disposition to this first test For the purpose of this study, we identify the latest test in the database to simulate it as the current, reported test under evaluation that the alg... | #13.1
list1 = np.unique(dfm_6m_ntt['ID_Profile'])
len(list1)
df_first_test = pd.DataFrame(columns=['ID_Profile','Test','DT_Specimen','quanttest','QualitativeResult','CD_Gender', 'Age', 'DT_Disposition', 'index_no'])
for i in list1:
IncTest = dfm_6m_ntt[dfm_6m_ntt['ID_Profile']==i]
time_IncTest = IncTest['DT_Di... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 14: A list (`profile_list`) is created with distinct unique identifiers. This profile list is used to loop in the program. A single unique identifier (`ID_Profile`) or a subset can be run by creating a list of the subset as `profile_list`. This is especially useful to debug the process and view a specific... | # Cell number 14
profile_list = dfm_6m_ntt['ID_Profile'].unique()
profile_list
len(profile_list) | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
An example of cell 14.1 to run a fraction of the dataset (for e.g. a cut-off date) instead of the entire dataset. This will be useful to see if there are any bugs or issues. | # Cell number 14.1
profile_list = profile_list[:100]
len(profile_list)
#baseline_date = pd.to_datetime('20190318', format='%Y%m%d') #---->change the date within the '' to what you'd like
#baseline_date
#df_main_6m_ntt = df_main_dd[df_main_dd['DT_Specimen'] >= baseline_date]
#df_main_6m_ntt = df_main_dd[(df_main_dd['DS... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
All of the code for the algorithm is below in cell number 1 Each loop corresponding to the Step number in the algorithm is assigned that loop number. Apart from the disposition assigned in the algorithm, tests are also assigned the exact decision point (for debugging and testing). Steps are numbered and commented at e... | count = 0
dispo_type = 0
col1 = ['ID_Profile','Test','DT_Specimen','quanttest','QualitativeResult','CD_Gender', 'Age']
col2 = col1.copy()
col2.append('index_no')
# final dataframe which is written to a file at the end
df_complete_merged = pd.DataFrame(columns = ['ID_Profile', 'Test_x', 'DT_Specimen_x', 'quanttest_x',
... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 16: The script below assigns 1 column for index number for the test. This index number is inherited from the original file df_main | df_complete_merged['test_index'] = df_complete_merged['index_no'].astype(str) + df_complete_merged['index_no_x'].astype(str)
df_complete_merged['test_index'] = df_complete_merged['test_index'].map(lambda x: x.lstrip('nan').rstrip('nan'))
df_complete_merged=df_complete_merged.drop(columns=['index_no', 'index_no_x'])
st... | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 17 creates a CSV file for this dataset. Please replace 'ResultOfAlgorithm.csv' with a file path and name of your choice. | # Cell number 17
df_complete_merged.to_csv(r'ResultOfAlgorithm.csv', index=False) | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 18We join the dataset generated by the algorithm script (`df_complete_merged`) on the main dataset (`df_main`). `test_index` and `S_No` are the same index inherited from `df_main`. We conduct a left join on this index number of both datasets because `Disposition` is assigned to the serology identified on `... | # Cell number 18
df_comp_merged_co = df_complete_merged.copy()
df_main_co = df_main.copy()
df_comp_merged_co['S_No'] = df_comp_merged_co['S_No'].astype(str)
df_main_co['S_No'] = df_main_co['S_No'].astype(str)
df_joined = df_comp_merged_co.merge(df_main_co, left_on='test_index',right_on = 'S_No', how='left') | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Cell number 19 creates a CSV file for this dataset. Please replace 'AlgorithmMerged.csv' with a file path and name of your choice. | # Cell number 19
df_joined.to_csv(r'AlgorithmMerged.csv', index=False) | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
This is the end of the script for the algorithm | df_complete_merged['dispo'].value_counts()
df_complete_merged['algo_dispo'].value_counts()
df_complete_merged['dis_type'].value_counts()
df_complete_merged.groupby(['Disposition','algo_dispo'])["ID_Profile"].count() | _____no_output_____ | Apache-2.0 | notebooks/CDC Syphilis Serology Prioritization Algorithm Script v3.ipynb | CDCgov/Syphilis_Record_Search_and_Review_Algorithm |
Work with DataData is the foundation on which machine learning models are built. Managing data centrally in the cloud, and making it accessible to teams of data scientists who are running experiments and training models on multiple workstations and compute targets is an important part of any professional data science ... | import azureml.core
from azureml.core import Workspace
# Load the workspace from the saved config file
ws = Workspace.from_config()
print('Ready to use Azure ML {} to work with {}'.format(azureml.core.VERSION, ws.name)) | Ready to use Azure ML 1.22.0 to work with azure_ds_challenge
| MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Work with datastoresIn Azure ML, *datastores* are references to storage locations, such as Azure Storage blob containers. Every workspace has a default datastore - usually the Azure storage blob container that was created with the workspace. If you need to work with data that is stored in different locations, you can ... | # Get the default datastore
default_ds = ws.get_default_datastore()
# Enumerate all datastores, indicating which is the default
for ds_name in ws.datastores:
print(ds_name, "- Default =", ds_name == default_ds.name) | azureml_globaldatasets - Default = False
workspacefilestore - Default = False
workspaceblobstore - Default = True
| MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Extra note: Considerations for datastoresWhen planning for datastores, consider the following guidelines: When using Azure blob storage, premium level storage may provide improved I/O performance for large datasets. However, this option will increase cost and may limit replication options for data redundancy. Wh... | default_ds.upload_files(files=['./data/diabetes.csv', './data/diabetes2.csv'], # Upload the diabetes csv files in /data
target_path='diabetes-data/', # Put it in a folder path in the datastore
overwrite=True, # Replace existing files of the same name
... | Uploading an estimated of 2 files
Uploading ./data/diabetes.csv
Uploaded ./data/diabetes.csv, 1 files out of an estimated total of 2
Uploading ./data/diabetes2.csv
Uploaded ./data/diabetes2.csv, 2 files out of an estimated total of 2
Uploaded 2 files
| MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Work with datasetsAzure Machine Learning provides an abstraction for data in the form of *datasets*. A dataset is a versioned reference to a specific set of data that you may want to use in an experiment. Datasets can be *tabular* or *file*-based. Create a tabular datasetLet's create a dataset from the diabetes data y... | from azureml.core import Dataset
# Get the default datastore
default_ds = ws.get_default_datastore()
#Create a tabular dataset from the path on the datastore (this may take a short while)
tab_data_set = Dataset.Tabular.from_delimited_files(path=(default_ds, 'diabetes-data/*.csv'))
# Display the first 20 rows as a Pa... | _____no_output_____ | MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Types of datasetDatasets are typically based on files in a datastore, though they can also be based on URLs and other sources. You can create the following types of dataset: Tabular: The data is read from the dataset as a table. You should use this type of dataset when your data is consistently structured and you w... | #Create a file dataset from the path on the datastore (this may take a short while)
file_data_set = Dataset.File.from_files(path=(default_ds, 'diabetes-data/*.csv'))
# Get the files in the dataset
for file_path in file_data_set.to_path():
print(file_path) | /diabetes.csv
/diabetes2.csv
| MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Register datasetsNow that you have created datasets that reference the diabetes data, you can register them to make them easily accessible to any experiment being run in the workspace.We'll register the tabular dataset as **diabetes dataset**, and the file dataset as **diabetes files**. | # Register the tabular dataset
try:
tab_data_set = tab_data_set.register(workspace=ws,
name='diabetes dataset',
description='diabetes data',
tags = {'format':'CSV'},
... | Datasets registered
| MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Retrieving a registered datasetAfter registering a dataset, you can retrieve it by using any of the following techniques: The datasets dictionary attribute of a Workspace object. The get_by_name or get_by_id method of the Dataset class.Both of these techniques are shown in the following code:```import azureml.co... | print("Datasets:")
for dataset_name in list(ws.datasets.keys()):
dataset = Dataset.get_by_name(ws, dataset_name)
print("\t", dataset.name, 'version', dataset.version) | Datasets:
diabetes file dataset version 1
diabetes dataset version 1
TD-Auto_Price_Training-Clean_Missing_Data-Cleaning_transformation-91e1ac5f version 1
TD-Auto_Price_Training-Normalize_Data-Transformation_function-0f39eb1a version 1
MD-Auto_Price_Training-Train_Model-Trained_model-559e7cee version 1
bike-... | MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Dataset versioningDatasets can be versioned, enabling you to track historical versions of datasets that were used in experiments, and reproduce those experiments with data in the same state.You can create a new version of a dataset by registering it with the same name as a previously registered dataset and specifying ... | import os
# Create a folder for the experiment files
experiment_folder = 'diabetes_training_from_tab_dataset'
os.makedirs(experiment_folder, exist_ok=True)
print(experiment_folder, 'folder created')
%%writefile $experiment_folder/diabetes_training.py
# Import libraries
import os
import argparse
from azureml.core impor... | Writing diabetes_training_from_tab_dataset/diabetes_training.py
| MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Extra note: Use a script argument for a tabular datasetYou can pass a tabular dataset as a script argument. When you take this approach, the argument received by the script is the unique ID for the dataset in your workspace. In the script, you can then get the workspace from the run context and use it to retrieve the ... | from azureml.core import Experiment, ScriptRunConfig, Environment
from azureml.core.conda_dependencies import CondaDependencies
from azureml.widgets import RunDetails
# Create a Python environment for the experiment
sklearn_env = Environment("sklearn-env")
# Ensure the required packages are installed (we need scikit... | _____no_output_____ | MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
> **Note:** The **--input-data** argument passes the dataset as a *named input* that includes a *friendly name* for the dataset, which is used by the script to read it from the **input_datasets** collection in the experiment run. The string value in the **--input-data** argument is actually the registered dataset's ID.... | from azureml.core import Model
run.register_model(model_path='outputs/diabetes_model.pkl', model_name='diabetes_model',
tags={'Training context':'Tabular dataset'}, properties={'AUC': run.get_metrics()['AUC'], 'Accuracy': run.get_metrics()['Accuracy']})
for model in Model.list(ws):
print(model.... | diabetes_model version: 3
Training context : Tabular dataset
AUC : 0.8568595320655352
Accuracy : 0.7891111111111111
diabetes_model version: 2
Training context : Parameterized script
AUC : 0.8484357430717946
Accuracy : 0.774
diabetes_model version: 1
Training context : Script
AUC : 0.8483203144435048... | MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Train a model from a file datasetYou've seen how to train a model using training data in a *tabular* dataset; but what about a *file* dataset?When you're using a file dataset, the dataset argument passed to the script represents a mount point containing file paths. How you read the data from these files depends on the... | import os
# Create a folder for the experiment files
experiment_folder = 'diabetes_training_from_file_dataset'
os.makedirs(experiment_folder, exist_ok=True)
print(experiment_folder, 'folder created')
%%writefile $experiment_folder/diabetes_training.py
# Import libraries
import os
import argparse
from azureml.core impo... | Writing diabetes_training_from_file_dataset/diabetes_training.py
| MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
Just as with tabular datasets, you can retrieve a file dataset from the **input_datasets** collection by using its friendly name. You can also retrieve it from the script argument, which in the case of a file dataset contains a mount path to the files (rather than the dataset ID passed for a tabular dataset).Next we ne... | from azureml.core import Experiment
from azureml.widgets import RunDetails
# Get the training dataset
diabetes_ds = ws.datasets.get("diabetes file dataset")
# Create a script config
script_config = ScriptRunConfig(source_directory=experiment_folder,
script='diabetes_training.py',
... | _____no_output_____ | MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
When the experiment has completed, in the widget, view the **azureml-logs/70_driver_log.txt** output log to verify that the files in the file dataset were downloaded to a temporary folder to enable the script to read the files. Register the trained modelOnce again, you can register the model that was trained by the exp... | from azureml.core import Model
run.register_model(model_path='outputs/diabetes_model.pkl', model_name='diabetes_model',
tags={'Training context':'File dataset'}, properties={'AUC': run.get_metrics()['AUC'], 'Accuracy': run.get_metrics()['Accuracy']})
for model in Model.list(ws):
print(model.nam... | diabetes_model version: 4
Training context : File dataset
AUC : 0.8568517900798176
Accuracy : 0.7891111111111111
diabetes_model version: 3
Training context : Tabular dataset
AUC : 0.8568595320655352
Accuracy : 0.7891111111111111
diabetes_model version: 2
Training context : Parameterized script
AUC :... | MIT | 06 - Work with Data.ipynb | ChidiNdego/azure-challenge-mslearn-dp100 |
We have a folder called `vrt`, however, we want to empty it first before writing new annotation files into it. The idea should be that we modify ELAN files, and the changes there result in update of old VRT files. Of course replacing all of them at once is not very effective, so there probably should be Git commit info... | folder = './vrt'
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception as e:
print(e) | _____no_output_____ | Apache-2.0 | ikdp2korp.ipynb | langdoc/eaf2korp |
`eaf2vrt` function is designed so that it takes an ELAN file and writes it into an arbitrary location as a VRT file. Thereby both input and output file have to be specified. Also ELAN tier that contains the annotations we want to work with has to be specified. | filenames = sorted(Path('/Users/niko/langdoc/kpv/').glob('**/kpv_izva*.eaf'))
for filename in filenames:
elan_file = str(filename)
session_name = Path(elan_file).stem
vrt_file = 'vrt/' + session_name + '.vrt'
eaf2korp.eaf2vrt(elan_file_path = elan_file, vrt_file_path = vrt_file)
| Parsing unknown version of ELAN spec... This could result in errors...
Parsing unknown version of ELAN spec... This could result in errors...
Parsing unknown version of ELAN spec... This could result in errors...
Parsing unknown version of ELAN spec... This could result in errors...
Parsing unknown version of ELAN spec... | Apache-2.0 | ikdp2korp.ipynb | langdoc/eaf2korp |
This leaves us with a folder full of VRT files. In this point we just have to specify the language which uralicNLP uses to run the analyser. | filenames = sorted(Path('vrt/').glob('*.vrt'))
for filename in filenames:
vrt_file = str(filename)
eaf2korp.annotate_vrt(vrt_file_path = vrt_file, language = "kpv")
| _____no_output_____ | Apache-2.0 | ikdp2korp.ipynb | langdoc/eaf2korp |
UT2000 Home Environment Exploratory AnalysisWe now take a closer look at the beacon and home environment survey data to see if we can tease anything out of it or at least show something. | import math | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Data ImportWe have two things to import: (1) the home environment survey and (2) the beacon data | import pandas as pd
import numpy as np
import os
from datetime import datetime | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Home Environment SurveyThe get the home environment survey data, please make sure to run ```$ python3 src/data/make_dataset.py``` and choose the option for the HEH survey. This will combine, clean, and save the home environment survey data to the processed data directory. | HEH = pd.read_csv(f'/Users/hagenfritz/Projects/utx000/data/processed/ut3000-heh.csv')
HEH.head() | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
BeaconsTo get the beacon data for the UT2000 study, be sure ture run ```$ python3 src/data/make_dataset.py``` and chooose the option for the ut2000 beacons. This will combine, clean, and save the beacon data to the processed data directory. | beacons = pd.read_csv(f'/Users/hagenfritz/Projects/utx000/data/processed/ut2000-beacon.csv',
index_col=0,parse_dates=True,infer_datetime_format=True)
beacons.head() | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Visualization and AnalysisNow we get to the meat of it - visualizing and doing some simple statistics on the data. | import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.colors import ListedColormap, LinearSegmentedColormap | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Heat Maps Investigating Participant 36dsqll3 (Beacon 6) who we used in the MADS Framework paper. There were some questions about the values reported in the Figure 3 which corresponds to 3/30. | fig,ax = plt.subplots(figsize=(8,6))
df = df_hm
sns.heatmap(data=df,cmap='inferno_r',cbar=True,square=False,ax=ax)
labels = []
for d in df.index:
dd = datetime(d.year,d.month,d.day)
if dd >= datetime(2019,3,18) and dd <= datetime(2019,3,23):
labels.append(datetime.strftime(d,'%a %m/%d')+'*')
else:
... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Seems the concentration spikes for whatever reason on 3/30 | def plot_heat_map(df):
'''
'''
fig,ax = plt.subplots(figsize=(8,6))
df = df/np.nanmax(df)
sns.heatmap(data=df,cmap='inferno_r',cbar=True,cbar_kws={'ticks':[]},square=False,ax=ax)
ax.text(25,27.4,'Minimum',bbox={'facecolor':'white'},zorder=10)
ax.text(25,-1,'Maximum',bbox={'facecolo... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
per BeaconThe following cell creates heat maps for each beacon for each sensor. | for b in [1,2,5]:
df = beacons[beacons['number'] == b]
df = df.resample('1h').mean()
df['hour'] = df.index.hour
df['day'] = df.index.date
df = df.dropna()
df = df[datetime(2019,3,16):datetime(2019,4,5,23)]
df_pm = df.pivot_table(index='day',columns='hour',values='pm2.5')
df_tc = df.pivot... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
per Beacon per SensorNow we get even more fine-grained and show the heat map for each sensor on each beacon. | for no in [1,2,5,6]:
df = beacons[beacons['number'] == no]
df = df.resample('1h').mean()
df['hour'] = df.index.hour
df['day'] = df.index.date
df = df.dropna()
df = df[datetime(2019,3,11):datetime(2019,4,5)]
df_hm = df.pivot_table(index='day',columns='hour',values='pm2.5')
if len(df_hm) >... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
EverythingNow we combine all the beacons and all the sensors into one heat map. We still normalize the data, but now we do it per sensor for all the beacons. | fig, ax = plt.subplots(4,4,figsize=(16,18),sharex='col',gridspec_kw={'width_ratios': [15, 15, 15, 1]})
row = 0
for value in ['pm2.5','TVOC','TC','RH']:
col = 0
for b in [1,2,5]:
# Getting heatmap dataframe
df = beacons[beacons['number'] == b]
df = df.resample('1h').mean()
df['hou... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Split Heatmap | def create_cmap(colors,nodes):
cmap = LinearSegmentedColormap.from_list("mycmap", list(zip(nodes, colors)))
return cmap | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
IAQ Heat MapThis heat map is NOT scaled but includes the actual values. | fig, ax = plt.subplots(2,3,figsize=(18,8),sharex='col',gridspec_kw={'width_ratios': [8, 8, 9]})
row = 0
for value in ['pm2.5','TVOC']:
col = 0
for b in [1,2,5]:
# Getting heatmap dataframe
df = beacons[beacons['number'] == b]
df = df.resample('1h').mean()
df['hour'] = df.index.ho... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
T/RH Heat MapThe T/RH values are scaled between 0 and 1. | fig, ax = plt.subplots(2,3,figsize=(18,8),sharex='col',gridspec_kw={'width_ratios': [8, 8, 9]})
row = 0
for value in ['TC','RH']:
col = 0
for b in [1,2,5]:
# Getting heatmap dataframe
df = beacons[beacons['number'] == b]
df = df.resample('1h').mean()
df['hour'] = df.index.hour
... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Beacon Covariance PlotThe covariance plot will give a nice look at the relationship between the four variables measured on the beacon2.0: PM, TVOC, Temperature, and RH. | # Cleaning up the dataframe
## Removing high values
df = beacons[beacons['TVOC'] < 800]
df = df[df['pm2.5'] < 300]
## Removing and renaming columns
df = df[['pm2.5','TVOC','TC','RH']]
df.columns = ['PM2.5 ($\mu$g/m$^3$)','TVOC (ppb)', 'Temperature ($^\circ$C)', 'Relative Humidity']
# Plotting
sns.pairplot(df,corner=Fal... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Floor Type and PM/TVOC | # Getting only the ut200 responses and simplifying the dataframe
heh2000 = HEH[HEH['study'] == 'ut2000']
floor2000 = heh2000[['record_id', 'amt_carpet','hardwd_amt', 'tile_amt','beacon']]
floor2000
# Plotting the distributions of air pollutants based on floor type
fig, ax = plt.subplots(2,1,figsize=(12,12))
heh2000.sor... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
Dwelling Type and PollutionNow we look at the different dwelling type and see if that has an effect on the pollutant concentration. | dwelling2000 = heh2000[['record_id', 'livingsit','beacon']]
dwelling2000
fig, ax = plt.subplots(2,1,figsize=(12,12))
for no in heh2000['beacon'].unique():
df = beacons[beacons['number'] == no]
# Removing high tvoc/pm values and resaving
df_tvoc = df[df['TVOC'] < 800]
df_pm = df[df['pm2.5'] < 300]
# ... | _____no_output_____ | MIT | notebooks/archive/2.0-hef-beacon-exploration.ipynb | intelligent-environments-lab/utx000 |
自动生成字幕 工作内容：- 从视频文件自动生成外挂型字幕文件，不进行视频编辑等操作- 包括，时间轴主要工作：- 获取视频文件中的音频文件- 将音频文件分段- 采用IBM CLOUD进行语音转文字，每月500分钟免费- 自动构建B站字幕后续工作：- 采用 翻译API，自动进行初步翻译 - google: googletrans包，因为墙pass - youdao: 翻译 api B站字幕文件 .bcc 格式 | # 示例
{
"font_size":0.4, # 字体大小
"font_color":"#FFFFFF", # 字体颜色
"background_alpha":0.5, # 背景透明度
"background_color":"#9C27B0", # 背景颜色
"Stroke":"none", #
"body":[ # 主体
{
"from":13, # 起始处
"to":14.95, # 结束处
"location":2, #
"content":"Hi I`m La... | _____no_output_____ | MIT | 自动生成字幕.ipynb | zhenghao0379/autosubpy |
获取视频转音频 | import moviepy.editor as mve
path = r'D:\Music\电吉他\Berklee - Modern Method for Guitar - Vol1 (DVD)\video\Lesson 1'
file = '001.mov'
filepath = path + "\\" + file
filepath
video = mve.VideoFileClip(filepath)
audio = video.audio
audio.write_audiofile('test.mp3')
audio.write_audiofile('test.mp3')
# import ffmpeg
# auto... | _____no_output_____ | MIT | 自动生成字幕.ipynb | zhenghao0379/autosubpy |
音频转文字 | # IBM Cloud Speech to text
# API密钥：-ijVpcr8DbYekYCkOEtalbfH6uMO1rI0qJAz0mEbDvre
# URL：https://api.us-south.speech-to-text.watson.cloud.ibm.com/instances/48476942-3322-4bba-aae3-18a2b84015d7 | _____no_output_____ | MIT | 自动生成字幕.ipynb | zhenghao0379/autosubpy |
文字构成字幕 翻译 | # 工具包
import requests
import json
# 有道翻译，调用有道翻译API(较不准，较快，稳定)
def youdao_trans(text, t_type='AUTO'):
text = text.replace(' ', '%20')
url = 'http://fanyi.youdao.com/translate?&doctype=json&type='+t_type+'&i='+text
# print(url)
header = {}
out = requests.get(url)
out_json = out.json()
# print(... | _____no_output_____ | MIT | 自动生成字幕.ipynb | zhenghao0379/autosubpy |
Morph source estimates from one subject to another subjectA source estimate from a given subject 'sample' is morphedto the anatomy of another subject 'fsaverage'. The outputis a source estimate defined on the anatomy of 'fsaverage' | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subject_from = 'sample'
su... | _____no_output_____ | BSD-3-Clause | 0.16/_downloads/plot_morph_data.ipynb | drammock/mne-tools.github.io |
 Copyright! This material is protected, please do not copy or distribute. by:Taher Assaf ***Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn *** 14.5 Shifting Data Through Time (Lagging and Leading) First we import pandas library: | import pandas as pd | _____no_output_____ | MIT | 14.5 Shifting Data Through Time (Lagging and Leading).ipynb | SiamakMushakhian/Numpy-Pandas-Seaborn |
Lets read a time series from a csv file using the function **pd.read_csv()**, we set the index column to be the date column using the argument **index_col**, and we convert the dates to datatime format using the argument **parse_dates = True**: | TS = pd.read_csv('data/ex17.csv', index_col = 'date', parse_dates = True)
TS | _____no_output_____ | MIT | 14.5 Shifting Data Through Time (Lagging and Leading).ipynb | SiamakMushakhian/Numpy-Pandas-Seaborn |
We can shift the price **one day** backward by using the function **shift()**: | TS.shift(periods = 1) | _____no_output_____ | MIT | 14.5 Shifting Data Through Time (Lagging and Leading).ipynb | SiamakMushakhian/Numpy-Pandas-Seaborn |
We can shift the price **two days** backward by using the function **shift()**: | TS.shift(periods = 2) | _____no_output_____ | MIT | 14.5 Shifting Data Through Time (Lagging and Leading).ipynb | SiamakMushakhian/Numpy-Pandas-Seaborn |
We can shift the price **three days** backward by using the function **shift()**: | TS.shift(periods = 3) | _____no_output_____ | MIT | 14.5 Shifting Data Through Time (Lagging and Leading).ipynb | SiamakMushakhian/Numpy-Pandas-Seaborn |
We do not need to write the name of the argument **(periods)**, we can just pass the number of periods like this: | TS.shift(3) | _____no_output_____ | MIT | 14.5 Shifting Data Through Time (Lagging and Leading).ipynb | SiamakMushakhian/Numpy-Pandas-Seaborn |
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