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 |
|---|---|---|---|---|---|
COUNTY HEALTH DEPARTMANT DATASETS | #HEALTH DEPARTMENTS DATA
flData=pd.read_csv('https://opendata.arcgis.com/datasets/222c9d85e93540dba523939cfb718d76_0.csv?outSR=%7B%22latestWkid%22%3A4326%2C%22wkid%22%3A4326%7D')
flData.to_csv('../_data/Data_Sources/Fl-Data.csv')
miData=pd.read_excel('https://www.michigan.gov/documents/coronavirus/Covid-19_Tests_by_Co... | _____no_output_____ | Apache-2.0 | _notebooks/Matrix Data Scrapper.ipynb | lalalaNomNomNom/Notebooks |
COVID TRACKER DATA | #COVID TRACKER DATA
da1=pd.read_html('https://covidtracking.com/data/state/alabama')
da1[1].to_csv('../_data/Data_Sources/CovidTracker/Alabama.csv')
da2=pd.read_html('https://covidtracking.com/data/state/alaska')
da2[1].to_csv('../_data/Data_Sources/CovidTracker/Alaska.csv')
da3=pd.read_html('https://covidtracking.c... | _____no_output_____ | Apache-2.0 | _notebooks/Matrix Data Scrapper.ipynb | lalalaNomNomNom/Notebooks |
NEW YORK TIMES DATA | #NEW YORK TIMES DATA
MASK_NYT=pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/mask-use/mask-use-by-county.csv')
MASK_NYT.to_csv('../_data/data_sources/NYT/MASKUSAGE-'+date+'.csv')
CASESDEATHSC_NYT=pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv')
... | _____no_output_____ | Apache-2.0 | _notebooks/Matrix Data Scrapper.ipynb | lalalaNomNomNom/Notebooks |
linlab | LINLAB=pd.read_csv('https://raw.githubusercontent.com/lin-lab/COVID19-Viz/master/clean_data/rt_table_export.csv')
LINLAB.to_csv('../data/sources/LINLAB/'+date+'-RTCOUNTYLEVEL.csv')
wew=requests.get('https://www.geonames.org/statistics/')
us=pd.read_html(wew.text)
us[1].to_csv('../_data/data_sources/worldCountryPopulati... | _____no_output_____ | Apache-2.0 | _notebooks/Matrix Data Scrapper.ipynb | lalalaNomNomNom/Notebooks |
# MIT License
# Copyright (c) 2019.
# Project Name: ANLP
# File name: code.py
# Last modification date: 2/28/19 2:32 PM
# Author: ramaseshan
# Email:ramaseshanr@yahoo.com
# /TTR.py
#
import nltk
from nltk.corpus import stopwords
#get the stop words for English
stop_words = set(stopwords.words('english'))
word... | 3826 7079
Number of tokens, Vocabulary, Type-token ratio (Bryant stories) = 15000 2796 0.1864
Number of tokens, Vocabulary, Type-token ratio (Jane Austen Emma) = 15000 3274 0.21826666666666666
| MIT | TTR.ipynb | Mukesh7197/anlp-1 | |
Import all libraries | import requests
import json
import time
import multiprocessing
import psycopg2 | _____no_output_____ | MIT | bitcoin_script_load_data_postgres.ipynb | himanshudce/Realtime-Database-with-Firebase |
Functions for loading the data and counting rows in meanwhile | # Postgres
def load_data_postgres(data):
start = time.time()
for line in data:
response_insert = requests.post('http://localhost:3001/postgres/write/crypto',json=line).text
end = time.time()
print("postgres",end-start)
# Firebase
def load_data_firebase(data):
start = time.time()
prev_ti... | _____no_output_____ | MIT | bitcoin_script_load_data_postgres.ipynb | himanshudce/Realtime-Database-with-Firebase |
Select which data to load | # # BITCOIN DATA
# json_file_path= './data/bitcoin_data_sample.json'
# with open(json_file_path) as json_file:
# bitcoin_data = json.load(json_file)
# # ETHEREUM DATA
# json_file_path= 'data/trajectory_data_60k.json'
# with open(json_file_path) as json_file:
# ethereum_data = json.load(json_file)
# # LITECOIN D... | _____no_output_____ | MIT | bitcoin_script_load_data_postgres.ipynb | himanshudce/Realtime-Database-with-Firebase |
Run process for Firebase |
p1 = multiprocessing.Process(target=load_data_postgres, args=(bitcoin_data, ))
p2 = multiprocessing.Process(target=load_data_postgres, args=(bitcoin_data, ))
p3 = multiprocessing.Process(target=load_data_postgres, args=(bitcoin_data, ))
p4 = multiprocessing.Process(target=time_measurement, args=(len(bitcoin_data), ))
... | _____no_output_____ | MIT | bitcoin_script_load_data_postgres.ipynb | himanshudce/Realtime-Database-with-Firebase |
Run process for PostgreSQL | load_data_postgres(data)
# BITCOIN DATA
json_file_path= './data/trajectory_data_60k.json'
with open(json_file_path) as json_file:
data = json.load(json_file)
len(data)
sql = """insert into trajectory_path(path_info) values(%s)"""
sql_count = """select count(*) from trajectory_path"""
start_time = time.ti... | 1637452712.3010046
2058
4605
7130
9652
12216
14725
17256
19806
22350
24918
27493
30004
32538
35064
37589
40129
42657
45291
47736
50190
52728
missile
CONTEXT: PL/pgSQL function process_dist_calculation() line 4 at RAISE
21.097785234451294
current transaction is aborted, commands ignored until end of transaction block
... | MIT | bitcoin_script_load_data_postgres.ipynb | himanshudce/Realtime-Database-with-Firebase |
PCA Reduction | from sklearn.decomposition import PCA
pca = PCA(n_components=0.99)
x_train_pca = pca.fit_transform(x_train)
x_valid_pca = pca.transform(x_valid)
def plot_faces(faces, label, n_rows = 4, n_cols = 5):
plt.figure(figsize=(8, 5))
for index, (face, label) in enumerate(zip(faces, label)):
plt.subplot(n_rows... | _____no_output_____ | MIT | exercise/chap_9/13_reconstruction_error.ipynb | vaibhavkumar11/hands-on-ml |
Modifying Images | from scipy import ndimage
# rotated, flipped and darkened the images
# flipping and darkening has been used from solution as turned out to be easier
x_transformed = []
for face in x_train[:20]:
transform = ndimage.rotate(face.reshape(64, 64), angle=np.random.choice([90, 180]),
... | _____no_output_____ | MIT | exercise/chap_9/13_reconstruction_error.ipynb | vaibhavkumar11/hands-on-ml |
The reconstruction error is not large | plot_faces(x_transformed, y_train[:20])
x_transformed_pca = pca.transform(x_transformed.reshape(-1, 4096))
plot_faces(pca.inverse_transform(x_transformed_pca), y_train[:20]) | _____no_output_____ | MIT | exercise/chap_9/13_reconstruction_error.ipynb | vaibhavkumar11/hands-on-ml |
Creating Extensions Using numpy and scipy=========================================**Author**: `Adam Paszke `_**Updated by**: `Adam Dziedzic `_In this tutorial, we shall go through two tasks:1. Create a neural network layer with no parameters. - This calls into **numpy** as part of its implementation2. Create a neur... | import torch
from torch.autograd import Function | _____no_output_____ | BSD-3-Clause | docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Parameter-less example----------------------This layer doesn’t particularly do anything useful or mathematicallycorrect.It is aptly named BadFFTFunction**Layer Implementation** | from numpy.fft import rfft2, irfft2
class BadFFTFunction(Function):
@staticmethod
def forward(ctx, input):
numpy_input = input.detach().numpy()
result = abs(rfft2(numpy_input))
return input.new(result)
@staticmethod
def backward(ctx, grad_output):
numpy_go = grad_outpu... | _____no_output_____ | BSD-3-Clause | docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
**Example usage of the created layer:** | input = torch.randn(8, 8, requires_grad=True)
result = incorrect_fft(input)
print(result)
result.backward(torch.randn(result.size()))
print(input) | _____no_output_____ | BSD-3-Clause | docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Parametrized example--------------------In deep learning literature, this layer is confusingly referredto as convolution while the actual operation is cross-correlation(the only difference is that filter is flipped for convolution,which is not the case for cross-correlation).Implementation of a layer with learnable wei... | from numpy import flip
import numpy as np
from scipy.signal import convolve2d, correlate2d
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
class ScipyConv2dFunction(Function):
@staticmethod
def forward(ctx, input, filter, bias):
# detach so we can cast to NumPy
... | _____no_output_____ | BSD-3-Clause | docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
**Example usage:** | module = ScipyConv2d(3, 3)
print("Filter and bias: ", list(module.parameters()))
input = torch.randn(10, 10, requires_grad=True)
output = module(input)
print("Output from the convolution: ", output)
output.backward(torch.randn(8, 8))
print("Gradient for the input map: ", input.grad) | _____no_output_____ | BSD-3-Clause | docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
**Check the gradients:** | from torch.autograd.gradcheck import gradcheck
moduleConv = ScipyConv2d(3, 3)
input = [torch.randn(20, 20, dtype=torch.double, requires_grad=True)]
test = gradcheck(moduleConv, input, eps=1e-6, atol=1e-4)
print("Are the gradients correct: ", test) | _____no_output_____ | BSD-3-Clause | docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Copyright 2018 The TensorFlow Authors.Licensed under the Apache License, Version 2.0 (the "License"); | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
TensorFlow 2.0 での tf.function と AutoGraph View on TensorFlow.org Run in Google Colab View source on GitHub Note: これらのドキュメントは私たちTensorFlowコミュニティが翻訳したものです。コミュニティによる 翻訳は**ベストエフォート**であるため、この翻訳が正確であることや[英語の公式ドキュメント](https://www.tensorflow.org/?hl=en)の 最新の状態を反映したものであることを保証することはできません。 この翻訳の品質を向上させるためのご意... | from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
!pip install tensorflow==2.0.0-beta1
import tensorflow as tf | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
`tf.function` デコレータ`tf.function`を用いてある関数にアノテーションを付けたとしても、一般の関数と変わらずに呼び出せます。一方、実行時にはその関数はグラフへとコンパイルされます。これにより、より高速な実行や、 GPU や TPU での実行、SavedModel へのエクスポートといった利点が得られます。 | @tf.function
def simple_nn_layer(x, y):
return tf.nn.relu(tf.matmul(x, y))
x = tf.random.uniform((3, 3))
y = tf.random.uniform((3, 3))
simple_nn_layer(x, y) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
アノテーションの結果を調べてみると、 TensorFlow ランタイムとのやり取りのすべてを処理する特別な呼び出し可能オブジェクトを確認できます。 | simple_nn_layer | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
記述したコードで複数の関数を利用していたとしても、すべての関数にアノテーションを付ける必要はありません。アノテーションをつけた関数から呼び出されるすべての関数は、グラフモードで実行されます。 | def linear_layer(x):
return 2 * x + 1
@tf.function
def deep_net(x):
return tf.nn.relu(linear_layer(x))
deep_net(tf.constant((1, 2, 3))) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
グラフが大量の軽量な演算から構成される場合、関数は Eager Execution で実行するコードよりも高速になる場合があります。しかし、 graph が少量の (畳み込み演算のような) 計算に時間のかかる演算からなる場合、高速化はそれほど見込めないでしょう。 | import timeit
conv_layer = tf.keras.layers.Conv2D(100, 3)
@tf.function
def conv_fn(image):
return conv_layer(image)
image = tf.zeros([1, 200, 200, 100])
# warm up
conv_layer(image); conv_fn(image)
print("Eager conv:", timeit.timeit(lambda: conv_layer(image), number=10))
print("Function conv:", timeit.timeit(lambda:... | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
Python の制御フローの利用`tf.function`の内部でデータに依存した制御フローを用いる場合、Pythonの制御フロー構文を用いることができます。AutoGraph はそれらの構文を TensorFlow の Ops に書き換えます。たとえば、 `Tensor` に依存する `if` 文は、`tf.cond()` に変換されます。次の例では `x` は `Tensor` です。ですが、 `if` 文は期待するどおりに動作しています。 | @tf.function
def square_if_positive(x):
if x > 0:
x = x * x
else:
x = 0
return x
print('square_if_positive(2) = {}'.format(square_if_positive(tf.constant(2))))
print('square_if_positive(-2) = {}'.format(square_if_positive(tf.constant(-2)))) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
Note: この例ではスカラー値を用いた単純な条件を用いています。実利用する場合、典型的にはバッチ処理が用いられます。 AutoGraph は `while`, `for`, `if`, `break`, `continue`, `return` といった典型的なPythonの構文をサポートしています。また、これらを入れ子にして利用する場合もサポートしています。つまり、`Tensor`を返す式を`while` 文や `if` 文の条件式として用いることが可能です。また、`for` 文で `Tensor` の要素に渡って反復することも可能です。 | @tf.function
def sum_even(items):
s = 0
for c in items:
if c % 2 > 0:
print(c)
continue
s += c
return s
sum_even(tf.constant([10, 12, 15, 20])) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
より高度な使い方をするユーザーのために、AutoGraph は低レベルAPIも提供しています。次の例では AutoGraph が生成したコードを確認できます。 | print(tf.autograph.to_code(sum_even.python_function)) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
次はより複雑な制御フローの例です。 | @tf.function
def fizzbuzz(n):
msg = tf.constant('')
for i in tf.range(n):
if tf.equal(i % 3, 0):
tf.print('Fizz')
elif tf.equal(i % 5, 0):
tf.print('Buzz')
else:
tf.print(i)
fizzbuzz(tf.constant(15)) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
Keras での AutoGraph の利用`tf.function` はオブジェクトのメソッドに対しても利用できます。たとえば、カスタムしたKeras モデルにデコレーターを適用できます、典型的には `call` 関数にアノテーションを付けることで実現できるでしょう。より詳細が必要な場合、`tf.keras` を確認してください。 | class CustomModel(tf.keras.models.Model):
@tf.function
def call(self, input_data):
if tf.reduce_mean(input_data) > 0:
return input_data
else:
return input_data // 2
model = CustomModel()
model(tf.constant([-2, -4])) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
副作用Eager モードのように、通常の場合 `tf.function` の中で、`tf.assign` や `tf.print` といった副作用のある命令を実行できます。また、実行時の順序を保つために、処理順について必要な依存関係を書き加えます。 | v = tf.Variable(5)
@tf.function
def find_next_odd():
v.assign(v + 1)
if tf.equal(v % 2, 0):
v.assign(v + 1)
find_next_odd()
v | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
例: シンプルなモデルの学習AutoGraph はこれまで見てきたよりもずっと多くの演算を TensorFlow の内部で実行できます。たとえば、学習のためのループ処理は単に制御フローなので、実際にそれを TensorFlow に持ち込んで処理できます。 データのダウンロード | def prepare_mnist_features_and_labels(x, y):
x = tf.cast(x, tf.float32) / 255.0
y = tf.cast(y, tf.int64)
return x, y
def mnist_dataset():
(x, y), _ = tf.keras.datasets.mnist.load_data()
ds = tf.data.Dataset.from_tensor_slices((x, y))
ds = ds.map(prepare_mnist_features_and_labels)
ds = ds.take(20000).shuf... | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
モデルの定義 | model = tf.keras.Sequential((
tf.keras.layers.Reshape(target_shape=(28 * 28,), input_shape=(28, 28)),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(10)))
model.build()
optimizer = tf.keras.optimizers.Adam() | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
学習のためのループ処理の定義 | compute_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
compute_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
def train_one_step(model, optimizer, x, y):
with tf.GradientTape() as tape:
logits = model(x)
loss = compute_loss(y, logits)
grads = tape.gradient(loss, model.tr... | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
バッチ処理実際のアプリケーションにおいて、処理をバッチにまとめることはパフォーマンスの観点から重要です。AutoGraphを用いるのにもっとも適しているコードは、制御フローを _バッチ_ の単位で決定するようなコードです。もし、個々の _要素_ の単位で制御を決定する場合、パフォーマンスを保つために batch API を試してみてください。一例として、次の Python コードががあったとします。 | def square_if_positive(x):
return [i ** 2 if i > 0 else i for i in x]
square_if_positive(range(-5, 5)) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
TensorFlowに同等の処理を行わせる場合、次のように記述したくなるかもしれません。 (これは実際には動作します!) | @tf.function
def square_if_positive_naive(x):
result = tf.TensorArray(tf.int32, size=x.shape[0])
for i in tf.range(x.shape[0]):
if x[i] > 0:
result = result.write(i, x[i] ** 2)
else:
result = result.write(i, x[i])
return result.stack()
square_if_positive_naive(tf.range(-5, 5)) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
しかし、この場合、次のように書くこともできます。 | def square_if_positive_vectorized(x):
return tf.where(x > 0, x ** 2, x)
square_if_positive_vectorized(tf.range(-5, 5)) | _____no_output_____ | Apache-2.0 | site/ja/beta/guide/autograph.ipynb | abviv/docs |
SageMaker Pipelines to Train a BERT-Based Text ClassifierIn this lab, we will do the following:* Define a set of Workflow Parameters that can be used to parametrize a Workflow Pipeline* Define a Processing step that performs cleaning and feature engineering, splitting the input data into train and test data sets* Defi... | %%html
<p><b>Shutting down your kernel for this notebook to release resources.</b></p>
<button class="sm-command-button" data-commandlinker-command="kernelmenu:shutdown" style="display:none;">Shutdown Kernel</button>
<script>
try {
els = document.getElementsByClassName("sm-command-button");
els[0].click();
}
... | _____no_output_____ | Apache-2.0 | 10_pipeline/00_Overview.ipynb | MarcusFra/workshop |
--- | root = '/network/group/aopp/predict/TIP016_PAXTON_RPSPEEDY/ML4L/ECMWF_files/raw/'
f = 'aquaDay_errorGTE03K_04km_2019-01-01.tif'
sat_xr = xr.open_rasterio(root+f) #load the temperature data array
sat_xr = sat_xr.rename({'x':'longitude','y':'latitude'})
time_delta = pd.to_timedelta(sat_xr.longitude.data/15,unit='H')
fi... | _____no_output_____ | MIT | notebooks/legacy/legacy/07. Check MC logic.ipynb | tomkimpson/ML4L |
!ls
!ls -l # 자세히 보기
!pwd
!ls -l ./sample_data # 디렉토리 내용물 자세히 보기
!ls -l ./Wholesale_customers_data.csv
import pandas as pd
df = pd.read_excel('./wholesale.xls') # y가 없는 데이터 불러와서 y 생성 후 붙이기
df.info()
df.head(5)
Y = df['label']
X = df.iloc[:,1:9]
Y.shape , X.shape
from sklearn.tree import DecisionTreeClassifier
dtree = ... | _____no_output_____ | Apache-2.0 | wholesale_decisionTree_classification.ipynb | Ethan-Jeong/test_machinelearning | |
Question break downin this code we will create a child class in which access parent class creat a class "student"following parameter"i. Nameii. Batch Noiii. Address" creat new child class "DevnationStudents"1.in which we will call parent class const save "course""marks" list as empty2.Add only those courses in the... | class Student:
def __init__(self,name,batch_no,address):
self.name = name
self.batch_no = batch_no
self.address = address
def set_name(self):
return self.name
def set_batch_no(self):
return self.batch_no
def set_adress(self):
return self.address
... | _____no_output_____ | Apache-2.0 | 2021_07_15/lab three.ipynb | kashaf874/lab_one |
GradientBoostingRegressor with MinMaxScaler
This Code template is for regression analysis using a simple GradientBoostingRegressor based on the Gradient Boosting Ensemble Learning Technique using MinMaxScalerfor Feature Rescaling. Required Packages | import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as se
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import r2_score, mean_a... | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
InitializationFilepath of CSV file | #filepath
file_path = "" | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
List of features which are required for model training . | #x_values
features=[] | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Target feature for prediction. | #y_value
target = '' | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Data FetchingPandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data manipulation and data analysis tools.We will use panda's library to read the CSV file using its storage path.And we use the head function to display the initial row or entry. | df=pd.read_csv(file_path)
df.head() | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Feature SelectionsIt is the process of reducing the number of input variables when developing a predictive model. Used to reduce the number of input variables to both reduce the computational cost of modelling and, in some cases, to improve the performance of the model.We will assign all the required input features to... | X = df[features]
Y = df[target] | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Data PreprocessingSince the majority of the machine learning models in the Sklearn library doesn't handle string category data and Null value, we have to explicitly remove or replace null values. The below snippet have functions, which removes the null value if any exists. And convert the string classes data in the da... | def NullClearner(df):
if(isinstance(df, pd.Series) and (df.dtype in ["float64","int64"])):
df.fillna(df.mean(),inplace=True)
return df
elif(isinstance(df, pd.Series)):
df.fillna(df.mode()[0],inplace=True)
return df
else:return df
def EncodeX(df):
return pd.get_du... | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Calling preprocessing functions on the feature and target set. | x=X.columns.to_list()
for i in x:
X[i]=NullClearner(X[i])
X=EncodeX(X)
Y=NullClearner(Y)
X.head() | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Correlation MapIn order to check the correlation between the features, we will plot a correlation matrix. It is effective in summarizing a large amount of data where the goal is to see patterns. | f,ax = plt.subplots(figsize=(18, 18))
matrix = np.triu(X.corr())
se.heatmap(X.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax, mask=matrix)
plt.show() | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Data SplittingThe train-test split is a procedure for evaluating the performance of an algorithm. The procedure involves taking a dataset and dividing it into two subsets. The first subset is utilized to fit/train the model. The second subset is used for prediction. The main motive is to estimate the performance of th... | X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 123)#performing datasplitting | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Data RescalingMinMaxScaler subtracts the minimum value in the feature and then divides by the range, where range is the difference between the original maximum and original minimum.We will fit an object of MinMaxScaler to train data then transform the same data via fit_transform(X_train) method, following which we wil... | minmax_scaler = MinMaxScaler()
X_train = minmax_scaler.fit_transform(X_train)
X_test = minmax_scaler.transform(X_test) | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
ModelGradient Boosting builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function. Model Tuning Parameters 1. loss : {‘ls’, ‘lad’, ‘huber’, ‘quantile’}... | # Build Model here
model = GradientBoostingRegressor(random_state = 123)
model.fit(X_train, y_train) | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Model AccuracyWe will use the trained model to make a prediction on the test set.Then use the predicted value for measuring the accuracy of our model.> **score**: The **score** function returns the coefficient of determination R2 of the prediction. | print("Accuracy score {:.2f} %\n".format(model.score(X_test,y_test)*100)) | Accuracy score 94.47 %
| Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
> **r2_score**: The **r2_score** function computes the percentage variablility explained by our model, either the fraction or the count of correct predictions. > **mae**: The **mean abosolute error** function calculates the amount of total error(absolute average distance between the real data and the predicted data) b... | y_pred=model.predict(X_test)
print("R2 Score: {:.2f} %".format(r2_score(y_test,y_pred)*100))
print("Mean Absolute Error {:.2f}".format(mean_absolute_error(y_test,y_pred)))
print("Mean Squared Error {:.2f}".format(mean_squared_error(y_test,y_pred))) | R2 Score: 94.47 %
Mean Absolute Error 3.02
Mean Squared Error 16.01
| Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Feature ImportancesThe Feature importance refers to techniques that assign a score to features based on how useful they are for making the prediction. | plt.figure(figsize=(8,6))
n_features = len(X.columns)
plt.barh(range(n_features), model.feature_importances_, align='center')
plt.yticks(np.arange(n_features), X.columns)
plt.xlabel("Feature importance")
plt.ylabel("Feature")
plt.ylim(-1, n_features) | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Prediction PlotFirst, we make use of a plot to plot the actual observations, with x_train on the x-axis and y_train on the y-axis.For the regression line, we will use x_train on the x-axis and then the predictions of the x_train observations on the y-axis. | plt.figure(figsize=(14,10))
plt.plot(range(20),y_test[0:20], color = "blue")
plt.plot(range(20),model.predict(X_test[0:20]), color = "red")
plt.legend(["Actual","prediction"])
plt.title("Predicted vs True Value")
plt.xlabel("Record number")
plt.ylabel(target)
plt.show() | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_MinMaxScaler.ipynb | surya2365/ds-seed |
Plagiarism Text DataIn this project, you will be tasked with building a plagiarism detector that examines a text file and performs binary classification; labeling that file as either plagiarized or not, depending on how similar the text file is when compared to a provided source text. The first step in working with an... | !wget https://s3.amazonaws.com/video.udacity-data.com/topher/2019/January/5c4147f9_data/data.zip
!unzip data
# import libraries
import pandas as pd
import numpy as np
import os | _____no_output_____ | MIT | Project_Plagiarism_Detection/.ipynb_checkpoints/1_Data_Exploration-checkpoint.ipynb | iahsanujunda/ml_sagemaker_studies |
This plagiarism dataset is made of multiple text files; each of these files has characteristics that are is summarized in a `.csv` file named `file_information.csv`, which we can read in using `pandas`. | csv_file = 'data/file_information.csv'
plagiarism_df = pd.read_csv(csv_file)
# print out the first few rows of data info
plagiarism_df.head(10) | _____no_output_____ | MIT | Project_Plagiarism_Detection/.ipynb_checkpoints/1_Data_Exploration-checkpoint.ipynb | iahsanujunda/ml_sagemaker_studies |
Types of PlagiarismEach text file is associated with one **Task** (task A-E) and one **Category** of plagiarism, which you can see in the above DataFrame. Five task types, A-EEach text file contains an answer to one short question; these questions are labeled as tasks A-E.* Each task, A-E, is about a topic that might... | # print out some stats about the data
print('Number of files: ', plagiarism_df.shape[0]) # .shape[0] gives the rows
# .unique() gives unique items in a specified column
print('Number of unique tasks/question types (A-E): ', (len(plagiarism_df['Task'].unique())))
print('Unique plagiarism categories: ', (plagiarism_df[... | Number of files: 100
Number of unique tasks/question types (A-E): 5
Unique plagiarism categories: ['non' 'cut' 'light' 'heavy' 'orig']
| MIT | Project_Plagiarism_Detection/.ipynb_checkpoints/1_Data_Exploration-checkpoint.ipynb | iahsanujunda/ml_sagemaker_studies |
You should see the number of text files in the dataset as well as some characteristics about the `Task` and `Category` columns. **Note that the file count of 100 *includes* the 5 _original_ wikipedia files for tasks A-E.** If you take a look at the files in the `data` directory, you'll notice that the original, source ... | # Show counts by different tasks and amounts of plagiarism
# group and count by task
counts_per_task=plagiarism_df.groupby(['Task']).size().reset_index(name="Counts")
print("\nTask:")
display(counts_per_task)
# group by plagiarism level
counts_per_category=plagiarism_df.groupby(['Category']).size().reset_index(name="... |
Task:
| MIT | Project_Plagiarism_Detection/.ipynb_checkpoints/1_Data_Exploration-checkpoint.ipynb | iahsanujunda/ml_sagemaker_studies |
It may also be helpful to look at this last DataFrame, graphically.Below, you can see that the counts follow a pattern broken down by task. Each task has one source text (original) and the highest number on `non` plagiarized cases. | import matplotlib.pyplot as plt
% matplotlib inline
# counts
group = ['Task', 'Category']
counts = plagiarism_df.groupby(group).size().reset_index(name="Counts")
plt.figure(figsize=(8,5))
plt.bar(range(len(counts)), counts['Counts'], color = 'blue') | UsageError: Line magic function `%` not found.
| MIT | Project_Plagiarism_Detection/.ipynb_checkpoints/1_Data_Exploration-checkpoint.ipynb | iahsanujunda/ml_sagemaker_studies |
说明： 给你一个大小为 m * n 的方阵 mat，方阵由若干军人和平民组成，分别用 1 和 0 表示。 请你返回方阵中战斗力最弱的 k 行的索引，按从最弱到最强排序。 如果第 i 行的军人数量少于第 j 行，或者两行军人数量相同但 i 小于 j，那么我们认为第 i 行的战斗力比第 j 行弱。 军人 总是 排在一行中的靠前位置，也就是说 1 总是出现在 0 之前。示例 1： 输入：mat = [[1,1,0,0,0], [1,1,1,1,0], [1,0,0,0,0], [1,1,0,0,0], [1,1,1,1,1]], ... | class Solution:
def kWeakestRows(self, mat, k):
rows, cols = len(mat), len(mat[0])
powers = {}
for i in range(rows):
sum_pow = self.count_power(mat[i])
powers[i] = sum_pow
print(powers)
res = []
powers = sorted(powers.items(), key = lambda x: x... | {0: 5, 1: 1, 2: 2, 3: 4, 4: 5}
| Apache-2.0 | Binary Search/1015/1337. The K Weakest Rows in a Matrix.ipynb | YuHe0108/Leetcode |
New to Plotly?Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).You can set up Plotly to work in [online](https://plot.ly/python/getting-started/initialization-for-online-pl... | import plotly
plotly.__version__ | _____no_output_____ | CC-BY-3.0 | _posts/python-v3/basic/table/table_sub.ipynb | bmb804/documentation |
Import CSV Data | import pandas as pd
import re
import plotly.plotly as py
import plotly.graph_objs as go
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Mining-BTC-180.csv')
# remove min:sec:millisec from dates
for i, row in enumerate(df['Date']):
p = re.compile(' 00:00:00')
datetime = p.split(df[... | _____no_output_____ | CC-BY-3.0 | _posts/python-v3/basic/table/table_sub.ipynb | bmb804/documentation |
Table and Right Aligned PlotsIn Plotly there is no native way to insert a Plotly Table into a Subplot. To do this, create your own `Layout` object and defining multiple `xaxis` and `yaxis` to split up the chart area into different domains. Then for the traces you wish to insert in your final chart, set their `xaxis` a... | import plotly.plotly as py
import plotly.graph_objs as go
table_trace1 = go.Table(
domain=dict(x=[0, 0.5],
y=[0, 1.0]),
columnwidth = [30] + [33, 35, 33],
columnorder=[0, 1, 2, 3, 4],
header = dict(height = 50,
values = [['<b>Date</b>'],['<b>Number<br>transactions</b>'... | _____no_output_____ | CC-BY-3.0 | _posts/python-v3/basic/table/table_sub.ipynb | bmb804/documentation |
Vertical Table and Graph Subplot | import plotly.plotly as py
import plotly.graph_objs as go
table_trace2 = go.Table(
domain=dict(x=[0, 1],
y=[0, 1.0]),
columnwidth = [30] + [33, 35, 33],
columnorder=[0, 1, 2, 3, 4],
header = dict(height = 50,
values = [['<b>Date</b>'],['<b>Hash Rate, TH/sec</b>'],
... | _____no_output_____ | CC-BY-3.0 | _posts/python-v3/basic/table/table_sub.ipynb | bmb804/documentation |
ReferenceSee https://plot.ly/python/reference/table for more information regarding chart attributes! For examples of Plotly Tables, see: https://plot.ly/python/table/ | from IPython.display import display, HTML
display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-not... | _____no_output_____ | CC-BY-3.0 | _posts/python-v3/basic/table/table_sub.ipynb | bmb804/documentation |
Name: Anjum Rohra OverviewBeing a popular finance journalist of Europe, everyone is waiting for the IT Salary Survey report you release every 3 years. The IT Sector is booming and the younger aspirants keep themselves updated with the trends by the beautiful visualization... | import plotly.express as px
import pandas as pd
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Output, Input | _____no_output_____ | Apache-2.0 | IT_Survey_Application/building_the_dash_application.ipynb | anjumrohra/Data_visualization_with_Plotly_and_Dash |
Importing data | data_18 = pd.read_csv('Survey_2018.csv')
data_19 = pd.read_csv('Survey_2019.csv')
data_20 = pd.read_csv('Survey_2020.csv') | _____no_output_____ | Apache-2.0 | IT_Survey_Application/building_the_dash_application.ipynb | anjumrohra/Data_visualization_with_Plotly_and_Dash |
Imputing the missing values 2020 imputation | data_20 = data_20.rename(columns = {'Position ': 'Position'})
data_20.isnull().sum()
data_20['Age'].fillna(data_20['Age'].mean(), inplace=True)
data_20['Gender'].fillna('Male', inplace=True)
data_20['Position'].fillna('Software Engineer', inplace=True)
data_20['Position'].replace({'Account Managet': 'Account Manager', ... | _____no_output_____ | Apache-2.0 | IT_Survey_Application/building_the_dash_application.ipynb | anjumrohra/Data_visualization_with_Plotly_and_Dash |
2018 imputation | data_18.isnull().sum()
data_18['Gender'].fillna('M', inplace=True)
data_18['City'].fillna('Berlin', inplace=True)
data_18['Position'].fillna('Java Developer', inplace=True)
data_18['Your level'].fillna('Senior', inplace=True)
data_18['Are you getting any Stock Options?'].fillna('No', inplace=True)
data_18['Main languag... | _____no_output_____ | Apache-2.0 | IT_Survey_Application/building_the_dash_application.ipynb | anjumrohra/Data_visualization_with_Plotly_and_Dash |
2019 imputation | data_19.drop('0',axis=1,inplace=True)
data_19.isnull().sum()
data_19['Seniority level'].fillna('Senior', inplace=True)
data_19['Position (without seniority)'].fillna('Backend Developer', inplace=True)
data_19['Your main technology / programming language'].fillna('Python', inplace=True)
data_19['Main language at work'].... | _____no_output_____ | Apache-2.0 | IT_Survey_Application/building_the_dash_application.ipynb | anjumrohra/Data_visualization_with_Plotly_and_Dash |
Data Visualization with Dash Application | app = dash.Dash(__name__)
app.layout = html.Div([
html.Div([
dcc.Dropdown(id='years', multi=False, clearable=False,
options=[{'label':x, 'value':x} for x in sorted(['2018','2019','2020'])],
value="2018")
],style={'width':'50%'}),
html.Div([
dcc.... | Dash is running on http://127.0.0.1:8001/
Dash is running on http://127.0.0.1:8001/
* Serving Flask app "__main__" (lazy loading)
* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
* Debug mode: off
| Apache-2.0 | IT_Survey_Application/building_the_dash_application.ipynb | anjumrohra/Data_visualization_with_Plotly_and_Dash |
연습문제- 인터파크 투어에서 조회한 결과를 MongoDB로 저장하기 컬렉션 : tour- 필드 : 이미지경로-img, 상세페이지경로-link, 상품명-title, 상품설명-desc, 가격-price headless로 크롬 실행```options = webdriver.ChromeOptions()options.add_argument("headless")options.add_argument("window-size=1920x1080")driver = webdriver.Chrome(executable_path='C:/Code/chromedriver.exe', options... | from selenium import webdriver as wd
driver = wd.Chrome(executable_path='C:/Code/chromedriver.exe')
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import time
from pymongo import MongoClient
client = M... | _____no_output_____ | MIT | database/08_pymongo.ipynb | dayoungMM/TIL |
Crypto ArbitrageIn this Challenge, you'll take on the role of an analyst at a high-tech investment firm. The vice president (VP) of your department is considering arbitrage opportunities in Bitcoin and other cryptocurrencies. As Bitcoin trades on markets across the globe, can you capitalize on simultaneous price dislo... | import pandas as pd
from pathlib import Path
%matplotlib inline | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Collect the DataTo collect the data that you’ll need, complete the following steps:Instructions. 1. Using the Pandas `read_csv` function and the `Path` module, import the data from `bitstamp.csv` file, and create a DataFrame called `bitstamp`. Set the DatetimeIndex as the Timestamp column, and be sure to parse and for... | # Read in the CSV file called "bitstamp.csv" using the Path module.
# The CSV file is located in the Resources folder.
# Set the index to the column "Date"
# Set the parse_dates and infer_datetime_format parameters
bitstamp = pd.read_csv(
Path("Resources/bitstamp.csv"),
index_col = "Timestamp",
parse_dates... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Step 2: Use the `head` (and/or the `tail`) function to confirm that Pandas properly imported the data. | # Use the head (and/or tail) function to confirm that the data was imported properly.
# YOUR CODE HERE
display(bitstamp.head())
display(bitstamp.tail()) | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Step 3: Repeat Steps 1 and 2 for `coinbase.csv` file. | # Read in the CSV file called "coinbase.csv" using the Path module.
# The CSV file is located in the Resources folder.
# Set the index to the column "Timestamp"
# Set the parse_dates and infer_datetime_format parameters
coinbase = pd.read_csv(
Path("Resources/coinbase.csv"),
index_col = "Timestamp",
parse_... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Prepare the DataTo prepare and clean your data for analysis, complete the following steps:1. For the bitstamp DataFrame, replace or drop all `NaN`, or missing, values in the DataFrame.2. Use the `str.replace` function to remove the dollar signs ($) from the values in the Close column.3. Convert the data type of the Cl... | # For the bitstamp DataFrame, replace or drop all NaNs or missing values in the DataFrame
# Displays the null count
display(bitstamp.isnull().sum())
# Uses fillna() to replace all null values with 0
bitstamp = bitstamp.fillna(0)
# Verifies nulls have been replaced
display(bitstamp.isnull().sum()) | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Step 2: Use the `str.replace` function to remove the dollar signs ($) from the values in the Close column. | # Display head to show before $ removed
display(bitstamp.head())
# Use the str.replace function to remove the dollar sign, $
bitstamp.loc[:,"Close"] = bitstamp.loc[:,"Close"].str.replace("$","")
# Display head to show after has been removed.
display(bitstamp.head()) | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Step 3: Convert the data type of the Close column to a `float`. | # Convert the Close data type to a float
# display type before change
display(bitstamp["Close"].dtypes)
# change the column type to float
bitstamp.loc[:, "Close"] = bitstamp.loc[:, "Close"].astype("float")
# display the new type
display(bitstamp["Close"].dtypes)
| _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Step 4: Review the data for duplicated values, and drop them if necessary. | # Review the data for duplicate values, and drop them if necessary
# Count the number of duplicates
display(bitstamp.duplicated().sum())
# Drop the duplicates
bitstamp = bitstamp.drop_duplicates()
# Count the number of duplicates
display(bitstamp.duplicated().sum()) | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Step 5: Repeat Steps 1–4 for the coinbase DataFrame. | # Step 1 - Remove the Nulls
# Displays the null count
display(coinbase.isnull().sum())
# Uses fillna() to replace all null values with 0
coinbase = coinbase.fillna(0)
# Verifies nulls have been replaced
display(coinbase.isnull().sum())
# Step 2 - Remove $ from Close column
# Display head to show before $ removed
... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Analyze the DataYour analysis consists of the following tasks: 1. Choose the columns of data on which to focus your analysis.2. Get the summary statistics and plot the data.3. Focus your analysis on specific dates.4. Calculate the arbitrage profits. Step 1: Choose columns of data on which to focus your analysis.Selec... | # Use loc or iloc to select `Timestamp (the index)` and `Close` from bitstamp DataFrame
bitstamp_sliced = bitstamp.iloc[:,[3]]
# Review the first five rows of the DataFrame
bitstamp_sliced.head()
# Use loc or iloc to select `Timestamp (the index)` and `Close` from coinbase DataFrame
coinbase_sliced = coinbase.iloc[:,[... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Step 2: Get summary statistics and plot the data.Sort through the time series data associated with the bitstamp and coinbase DataFrames to identify potential arbitrage opportunities. To do so, complete the following steps:1. Generate the summary statistics for each DataFrame by using the `describe` function.2. For eac... | # Generate the summary statistics for the bitstamp DataFrame
bitstamp_sliced.describe()
# Generate the summary statistics for the coinbase DataFrame
coinbase_sliced.describe()
# Create a line plot for the bitstamp DataFrame for the full length of time in the dataset
# Be sure that the figure size, title, and color ar... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
**Question** Based on the visualizations of the different time periods, has the degree of spread change as time progressed?**Answer** Yes. Early in the timeframe, there were more frequent and more severe divergences between the price of Bitcoin on Bitstamp and Coinbase. Step 3: Focus Your Analysis on Specific DatesFo... | # Create an overlay plot that visualizes the two dataframes over a period of one day early in the dataset.
# Be sure that the plots include the parameters `legend`, `figsize`, `title`, `color` and `label`
coinbase_sliced["Close"].loc["2018-01-24" : "2018-01-24"].plot(legend = True, figsize=(15, 7), title="Jan. 25, 20... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Step 4: Calculate the Arbitrage ProfitsCalculate the potential profits for each date that you selected in the previous section. Your goal is to determine whether arbitrage opportunities still exist in the Bitcoin market. Complete the following steps:1. For each of the three dates, measure the arbitrage spread between ... | # For the date early in the dataset, measure the arbitrage spread between the two exchanges
# by subtracting the lower-priced exchange from the higher-priced one
# Determines the arbitrage spread at three different times - early, middle, and late
arbitrage_spread_early = bitstamp_sliced['Close'].loc['2018-01-24'] - c... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
2. For each of the three dates, calculate the spread returns. To do so, divide the instances that have a positive arbitrage spread (that is, a spread greater than zero) by the price of Bitcoin from the exchange you’re buying on (that is, the lower-priced exchange). Review the resulting DataFrame. | # For the date early in the dataset, calculate the spread returns by dividing the instances when the arbitrage spread is positive (> 0)
# by the price of Bitcoin from the exchange you are buying on (the lower-priced exchange).
spread_return_early= spread_return_early_positive / coinbase_sliced['Close'].loc['2018-01-24... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
3. For each of the three dates, narrow down your trading opportunities even further. To do so, determine the number of times your trades with positive returns exceed the 1% minimum threshold that you need to cover your costs. | # For the date early in the dataset, determine the number of times your trades with positive returns
# exceed the 1% minimum threshold (.01) that you need to cover your costs
profitable_trades_early = spread_return_early[spread_return_early > .01]
profitable_trades_middle = spread_return_middle[spread_return_middle > ... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
4. Generate the summary statistics of your spread returns that are greater than 1%. How do the average returns compare among the three dates? | # For the date early in the dataset, generate the summary statistics for the profitable trades
# or you trades where the spread returns are are greater than 1%
display(profitable_trades_early.describe())
display(profitable_trades_middle.describe())
display(profitable_trades_late.describe()) | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
5. For each of the three dates, calculate the potential profit, in dollars, per trade. To do so, multiply the spread returns that were greater than 1% by the cost of what was purchased. Make sure to drop any missing values from the resulting DataFrame. | # For the date early in the dataset, calculate the potential profit per trade in dollars
# Multiply the profitable trades by the cost of the Bitcoin that was purchased
profit_early = spread_return_early[spread_return_early > .01] * coinbase_sliced['Close'].loc['2018-01-24']
profit_middle = spread_return_middle[spread_... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
6. Generate the summary statistics, and plot the results for each of the three DataFrames. | # Generate the summary statistics for the early profit per trade DataFrame
display(profit_per_trade_early.describe())
display(profit_per_trade_middle.describe())
display(profit_per_trade_late.describe())
# Plot the results for the early profit per trade DataFrame
profit_per_trade_early.plot(legend = True, figsize=(15, ... | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
7. Calculate the potential arbitrage profits that you can make on each day. To do so, sum the elements in the profit_per_trade DataFrame. | # Calculate the sum of the potential profits for the early profit per trade DataFrame
display(profit_per_trade_early.sum())
display(profit_per_trade_middle.sum())
display(profit_per_trade_late.sum()) | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
8. Using the `cumsum` function, plot the cumulative sum of each of the three DataFrames. Can you identify any patterns or trends in the profits across the three time periods? | # Use the cumsum function to calculate the cumulative profits over time for the early profit per trade DataFrame
cumulative_profit_early = profit_per_trade_early.cumsum()# YOUR CODE HERE
# Plot the cumulative sum of profits for the early profit per trade DataFrame
cumulative_profit_early.plot() | _____no_output_____ | MIT | .ipynb_checkpoints/crypto_arbitrage-checkpoint.ipynb | justcb/bitcoin_arb |
Design MERFISH probes using the example inputs from Jeff Moffitt. The original MATLAB design pipeline can be found at https://github.com/ZhuangLab/MERFISH_analysis. Prepare inputs | # Download the input data
# This is for the UNIX-like operating systems. If you are using Windows, just download the files accordingly.
!mkdir temporary_data
!wget http://zhuang.harvard.edu/merfish/MERFISHData/MERFISH_Examples2.zip -O temporary_data/MERFISH_Examples2.zip
!unzip -o temporary_data/MERFISH_Examples2.zip -... | \\10.245.74.212\Chromatin_NAS_2\Libraries\CTP-10_Aire\MERFISH_designer\MERFISH_Examples2\transcripts.fasta
| MIT | Library_design/CTP-10_Aire/Merfish_design_from_XJP.ipynb | shiwei23/Chromatin_Analysis_Scripts |
Initialize data structures | # Import the modules
import os
import sys
from IPython.display import display
import MERFISH_probe_design
import MERFISH_probe_design.IO.file_io as fio
import MERFISH_probe_design.probe_design.probe_dict as p_d
import MERFISH_probe_design.probe_design.OTTable_dict as ot
import MERFISH_probe_design.probe_design.readou... | _____no_output_____ | MIT | Library_design/CTP-10_Aire/Merfish_design_from_XJP.ipynb | shiwei23/Chromatin_Analysis_Scripts |
load transcriptome | # Load the transcriptome as a pandas data frame
transcriptome = fio.load_transcriptome(transcripts_fasta_file, fpkm_tracking_file)
# Make sure that the transcriptome data frame has the standard column names.
# The standard columns are: transcript_id, sequence, gene_id, gene_short_name and FPKM.
# Also remove the non-s... | Load the OTTable from \\10.245.74.212\Chromatin_NAS_2\Libraries\CTP-10_Aire\MERFISH_designer\MERFISH_Examples2\outputs\ottable_transcriptome.pkl.
Wall time: 1min 8s
| MIT | Library_design/CTP-10_Aire/Merfish_design_from_XJP.ipynb | shiwei23/Chromatin_Analysis_Scripts |
Select target regions | # Calculate and plot the GC contents of the target regions
filters.calc_gc_for_probe_dict(probe_dict, column_key_seq='target_sequence', column_key_write='target_GC')
plot.plot_hist(probe_dict, column_key='target_GC')
# Filter GC cotent and plot the GC content after filtering
filters.filter_probe_dict_by_metric(probe_di... | VPS13D
ENST00000613099.4: 9512 / 9545 probes passed the filter 0.75 < target_specificity < inf.
PRDM2
ENST00000343137.8: 2806 / 2863 probes passed the filter 0.75 < target_specificity < inf.
LUZP1
ENST00000418342.5: 5478 / 5478 probes passed the filter 0.75 < target_specificity < inf.
CNR2
ENST00000374472.4: 249... | MIT | Library_design/CTP-10_Aire/Merfish_design_from_XJP.ipynb | shiwei23/Chromatin_Analysis_Scripts |
Design readout sequences | # Load the readout sequences into a data frame
readout_seqs = fio.load_fasta_into_df(readout_fasta_file)
rs.append_on_bit_ids_to_readout_sequences(readout_seqs, bit_names)
readout_seqs
# Add the readout sequences. Here we randomly add 3 readout sequences to each probe.
# Add an "A" between the concatenated sequences.
... | _____no_output_____ | MIT | Library_design/CTP-10_Aire/Merfish_design_from_XJP.ipynb | shiwei23/Chromatin_Analysis_Scripts |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.