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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merge validation pdfs created so far | # ! pip install PyPDF2
# ! ls -lh /home/jovyan/*.pdf
pdf_list = ['/home/jovyan/global_mean_tasmax_370.pdf',
'/home/jovyan/tasmax_max_bias_corrected.pdf',
'/home/jovyan/tasmax_max_cmip6.pdf',
'/home/jovyan/tasmax_max_downscaled.pdf',
'/home/jovyan/tasmax_mean_bias_corr... | _____no_output_____ | MIT | notebooks/downscaling_pipeline/global_validation.ipynb | brews/downscaleCMIP6 |
CSX46 - Class 19 - MCODEIn this notebook, we will analyze a simple graph (`test.dot`) and then the Krogran network using the MCODE community detection algorithm. | import pygraphviz
import igraph
import numpy
import pandas
import sys
from collections import defaultdict
test_graph = FILL IN HERE
nodes = test_graph.nodes()
edges = FILL IN HERE
test_igraph = FILL IN HERE
test_igraph.summary()
igraph.drawing.plot(FILL IN HERE) | _____no_output_____ | Apache-2.0 | class19_MCODE_python3_template.ipynb | curiositymap/Networks-in-Computational-Biology |
Function `mcode` takes a graph adjacency list `adj_list` and a float parameter `vwp` (vertex weight probability), and returns a list of cluster assignments (of length equal to the number of clusters). Original code from True Price at UNC Chapel Hill [link to original code](https://github.com/trueprice/python-graph-clu... | def mcode(adj_list, vwp):
# Stage 1: Vertex Weighting
N = len(adj_list)
edges = [[]]*N
weights = dict((v, 1.) for v in range(0,N))
edges=defaultdict(set)
for i in range(0,N):
edges[i] = # MAKE A SET FROM adj_list[i]
res_clusters = []
for i,v in enumerate(edges):
neighborhood = # union ... | _____no_output_____ | Apache-2.0 | class19_MCODE_python3_template.ipynb | curiositymap/Networks-in-Computational-Biology |
Run mcode on the adjacency list for your toy graph, with vwp=0.8. How many clusters did it find? Do the cluster memberships make sense? Load the Krogan et al. network edge-list data as a Pandas data frame | edge_list = pandas.read_csv("shared/krogan.sif",
sep="\t",
names=["protein1","protein2"]) | _____no_output_____ | Apache-2.0 | class19_MCODE_python3_template.ipynb | curiositymap/Networks-in-Computational-Biology |
Make an igraph graph and print its summary | krogan_graph = FILL IN HERE
krogan_graph.summary() | _____no_output_____ | Apache-2.0 | class19_MCODE_python3_template.ipynb | curiositymap/Networks-in-Computational-Biology |
Run mcode on your graph with vwp=0.1 | res = FILL IN HERE | _____no_output_____ | Apache-2.0 | class19_MCODE_python3_template.ipynb | curiositymap/Networks-in-Computational-Biology |
Get the cluster sizes | FILL IN HERE | _____no_output_____ | Apache-2.0 | class19_MCODE_python3_template.ipynb | curiositymap/Networks-in-Computational-Biology |
Test Hypothesis by Simulating Statistics Mini-Lab 1: Hypothesis Testing Welcome to your next mini-lab! Go ahead an run the following cell to get started. You can do that by clicking on the cell and then clickcing `Run` on the top bar. You can also just press `Shift` + `Enter` to run the cell. | from datascience import *
import numpy as np
import otter
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
grader = otter.Notebook("m7_l1_tests") | _____no_output_____ | MIT | minilabs/test-hypothesis-by-simulating-statistics/m7_l1.ipynb | garath/inferentialthinking |
In the previous two labs we've analyzed some data regarding COVID-19 test cases. Let's continue to analyze this data, specifically _claims_ about this data. Once again, we'll be be using ficitious statistics from Blockeley University.Let's say that Blockeley data science faculty are looking at the spread of COVID-19 ac... | test_results = Table.read_table("../datasets/covid19_village_tests.csv")
test_results.show(5)
... | _____no_output_____ | MIT | minilabs/test-hypothesis-by-simulating-statistics/m7_l1.ipynb | garath/inferentialthinking |
From here we can formulate our **Null Hypothesis** and **Alternate Hypothesis** Our *null hypothesis* is that this village truly has a 26% infection rate amongst the populations. Our *alternate hypothesis* is that this village does not in actuality have a 26% infection rate - it's way too low. Now we need our test stat... | def proportion_positive(test_results):
numerator = ...
denominator = ...
return numerator / denominator
grader.check("q1") | _____no_output_____ | MIT | minilabs/test-hypothesis-by-simulating-statistics/m7_l1.ipynb | garath/inferentialthinking |
If you grouped by `Village Number` before, you would realize that there are roughly 3000 tests per village. Let's now create functions that will randomly take 3000 tests from the `test_results` table and to apply our test statistic. Complete the `sample_population` and `apply_statistic` functions below!The `sample_popu... | def sample_population(population_table):
sampled_population = ...
return sampled_population
def apply_statistic(sample_table, column_name, statistic_function):
return statistic_function(...)
grader.check("q2") | _____no_output_____ | MIT | minilabs/test-hypothesis-by-simulating-statistics/m7_l1.ipynb | garath/inferentialthinking |
Now for the simulation portion! Complete the for loop below and fill in a reasonable number for the `iterations` variable. The `iterations` variable will determine just how many random samples that we will take in order to test our hypotheses. There is also code that will visualize your simulation and give you data reg... | # Simulation code below. Fill out this portion!
iterations = ...
simulations = make_array()
for iteration in np.arange(iterations):
sample_table = ...
test_statistic = ...
simulations = np.append(simulations, test_statistic)
# This code is to tell you what percentage of our simulations are at or bel... | _____no_output_____ | MIT | minilabs/test-hypothesis-by-simulating-statistics/m7_l1.ipynb | garath/inferentialthinking |
Given our hypothesis test, what can you conclude about the village that reports having a 26% COVID-19 infection rate? Has your hypothesis changed before? Do you now trust or distrust these numbers? And if you do distrust these numbers, what do you think went wrong in the reporting? Congratulations on finishing! Run the... | grader.check_all() | _____no_output_____ | MIT | minilabs/test-hypothesis-by-simulating-statistics/m7_l1.ipynb | garath/inferentialthinking |
Given running cost $g(x_t,u_t)$ and terminal cost $h(x_T)$ the finite horizon $(t=0 \ldots T)$ optimal control problem seeks to find the optimal control, $$u^*_{1:T} = \text{argmin}_{u_{1:T}} L(x_{1:T},u_{1:T})$$ $$u^*_{1:T} = \text{argmin}_{u_{1:T}} h(x_T) + \sum_{t=0}^T g(x_t,u_t)$$subject to the dynamics constraint:... | # NN parameters
Nsamples = 10000
epochs = 500
latent_dim = 1024
batch_size = 8
lr = 3e-4
# Torch environment wrapping gym pendulum
torch_env = Pendulum()
# Test parameters
Nsteps = 100
# Set up model (fully connected neural network)
model = FCN(latent_dim=latent_dim,d=torch_env.d,ud=torch_env.ud)
optimizer = torch.... | _____no_output_____ | MIT | Model-based-OC-shooting.ipynb | mgb45/OC-notebooks |
Генерация заголовков научных статей: слабый baseline Источник: https://github.com/bentrevett/pytorch-seq2seq | # Если Вы запускаете ноутбук на colab,
# выполните следующие строчки, чтобы подгрузить библиотеку dlnlputils:
# !git clone https://github.com/Samsung-IT-Academy/stepik-dl-nlp.git
# import sys; sys.path.append('/content/stepik-dl-nlp')
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.funct... | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
Обучение модели | import matplotlib
matplotlib.rcParams.update({'figure.figsize': (16, 12), 'font.size': 14})
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import clear_output
def train(model, iterator, optimizer, criterion, clip, train_history=None, valid_history=None):
model.train()
epoch_... | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
Finally, we load the parameters from our best validation loss and get our results on the test set. | # for cpu usage
model.load_state_dict(torch.load(MODEL_NAME, map_location=torch.device('cpu')))
# for gpu usage
# model.load_state_dict(torch.load(MODEL_NAME), map_location=torch.device('cpu'))
test_loss = evaluate(model, test_iterator, criterion)
print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss... | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
Генерация заголовков | def translate_sentence(model, tokenized_sentence):
model.eval()
tokenized_sentence = ['<sos>'] + [t.lower() for t in tokenized_sentence] + ['<eos>']
numericalized = [TEXT.vocab.stoi[t] for t in tokenized_sentence]
sentence_length = torch.LongTensor([len(numericalized)]).to(device)
tensor = torch.L... | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
Считаем BLEU на train.csv | import nltk
n_gram_weights = [0.3334, 0.3333, 0.3333]
test_len = len(test_data)
original_texts = []
generated_texts = []
macro_bleu = 0
for example_idx in range(test_len):
src = vars(test_data.examples[example_idx])['src']
trg = vars(test_data.examples[example_idx])['trg']
translation, _ = translate_sente... | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
Делаем submission в Kaggle | import pandas as pd
submission_data = pd.read_csv('datasets/test.csv')
abstracts = submission_data['abstract'].values | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
Генерация заголовков для тестовых данных: | titles = []
for abstract in abstracts:
title, _ = translate_sentence(model, abstract.split())
titles.append(' '.join(title).replace('<unk>', '')) | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
Записываем полученные заголовки в файл формата `,`: | submission_df = pd.DataFrame({'abstract': abstracts, 'title': titles})
submission_df.to_csv('datasets/predicted_titles.csv', index=False) | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
С помощью скрипта `generate_csv` приводим файл `submission_prediction.csv` в формат, необходимый для посылки в соревнование на Kaggle: | from create_submission import generate_csv
generate_csv('datasets/predicted_titles.csv', 'datasets/kaggle_pred.csv', 'datasets/vocs.pkl')
!wc -l datasets/kaggle_pred.csv
!head datasets/kaggle_pred.csv | _____no_output_____ | MIT | task11_kaggle/lstm_baseline.ipynb | yupopov/stepik-dl-nlp |
Basic Apach Spark Analysis
- Ref: https://timw.info/ply
- Notebook tutorial: https://timw.info/ekt
| # Load NYC Taxi data
df = spark.read.load('abfss://defaultfs@twsynapsedls.dfs.core.windows.net/NYCTripSmall.parquet', format='parquet')
display(df.limit(10))
# View the dataframe schema
df.printSchema()
# Load the NYC Taxi data into the Spark nyctaxi database
spark.sql("CREATE DATABASE IF NOT EXISTS nyctaxi")
df.... | _____no_output_____ | MIT | demo-resources/Spark-Pool-Notebook.ipynb | vijrqrr9/dp203 |
Germany: LK Kleve (Nordrhein-Westfalen)* Homepage of project: https://oscovida.github.io* [Execute this Jupyter Notebook using myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Germany-Nordrhein-Westfalen-LK-Kleve.ipynb) | import datetime
import time
start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
overview(country="Germany", subregion="LK Kleve");
# load the data
cases, deaths, region... | _____no_output_____ | CC-BY-4.0 | ipynb/Germany-Nordrhein-Westfalen-LK-Kleve.ipynb | RobertRosca/oscovida.github.io |
Explore the data in your web browser- If you want to execute this notebook, [click here to use myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Germany-Nordrhein-Westfalen-LK-Kleve.ipynb)- and wait (~1 to 2 minutes)- Then press SHIFT+RETURN to advance code cell to code cell- See http://jupyte... | print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
f"deaths at {fetch_deaths_last_execution()}.")
# to force a fresh download of data, run "clear_cache()"
print(f"Notebook execution took: {datetime.datetime.now()-start}")
| _____no_output_____ | CC-BY-4.0 | ipynb/Germany-Nordrhein-Westfalen-LK-Kleve.ipynb | RobertRosca/oscovida.github.io |
MNIST learns your handwritingThis is a small project on using a GAN to generate numbers that look as someone else's handwriting when not trained on all numbers written by this person. For example say we had someone write the number 273 and we now want to write 481 in their own handwriting.The main inspiration for thi... | import tensorflow as tf
from tensorflow_addons.layers import InstanceNormalization
import numpy as np
import tensorflow.keras.layers as layers
import time
from tensorflow.keras.datasets.mnist import load_data
import sys
import os
import datetime | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
LayersThere are a few layers that were custom made. More importantly it is udeful to make this custom layers for the layers that try to incorporate style. This is as the inputs themselves are custom as you are inputing an image and a vector representing the style.ResBlk is short for Residual Block, where it is predict... | class ResBlk(tf.keras.Model):
def __init__(self, dim_in, dim_out, actv=layers.LeakyReLU(),
normalize=False, downsample=False):
super(ResBlk, self).__init__()
self.actv = actv
self.normalize = normalize
self.downsample = downsample
self.learned_sc = dim_in != dim_out
self._buil... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
AdaIN stands for Adaptive Instance Normalization. It is a type of normalization that allows to 'mix' two inputs. In this case we use the style vector to mix with our input x which is the image or part of the process of constructing this image. | class AdaIn(tf.keras.Model):
def __init__(self, style_dim, num_features):
super(AdaIn,self).__init__()
self.norm = InstanceNormalization()
self.lin = layers.Dense(num_features*2)
def call(self, x, s):
h=self.lin(s)
h=tf.reshape(h, [1, tf.shape(h)[0], 1, tf.shape(h)[1]])
gamma,beta=tf.split(... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Generator ClassIn the generator we have two steps one for encoding the image into lower level information and one to decode back to the image. In this particular architecture the decoding uses the style to build back the image as it is an important part of the process. The decoding does not do this as we have the styl... | class Generator(tf.keras.Model):
def __init__(self, img_size=28, style_dim=24, dim_in=8, max_conv_dim=128, repeat_num=2):
super(Generator, self).__init__()
self.img_size=img_size
self.from_bw=layers.Conv2D(dim_in, 3, padding='same', input_shape=(1,img_size,img_size,1))
self.encode=[]
self.decode=[... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Mapping NetworkThe Mapping Network and the Style encoder are the parts of this architecture that make a difference in allowing style to be analyzed and put into our images. The mapping network will take as an input a latent code (represents images as a vector in a high dimensional space) and the domain in this case th... | class MappingNetwork(tf.keras.Model):
def __init__(self, latent_dim=16, style_dim=24, num_domains=10):
super(MappingNetwork,self).__init__()
map_layers = [layers.Dense(128)]
map_layers += [layers.ReLU()]
for _ in range(2):
map_layers += [layers.Dense(128)]
map_layers += [layers.ReLU()]
... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Style EncoderAn important thing to notice from the style encoder is that it takes as an input an image and outputs a style vector. Looking at the dimensions of these we notice we need to flatten out the image through the layers. This can usually be done in two ways. By flattening a 2 dimensional input to a 1 dimension... | class StyleEncoder(tf.keras.Model):
def __init__(self, img_size=28, style_dim=24, dim_in=16, num_domains=10, max_conv_dim=128, repeat_num=5):
super(StyleEncoder,self).__init__()
blocks = [layers.Conv2D(dim_in, 3, padding='same')]
for _ in range(repeat_num): #repetition 1 sends to (b,14,14,d) 2 ... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Discriminator ClassSimilarly to the Style encoder the input of the discriminator is an image and we need to downsample it until it is one dimensional. | class Discriminator(tf.keras.Model):
def __init__(self, img_size=28, dim_in=16, num_domains=10, max_conv_dim=128, repeat_num=5):
super(Discriminator, self).__init__()
blocks = [layers.Conv2D(dim_in, 3, padding='same')]
for _ in range(repeat_num): #repetition 1 sends to (b,14,14,d) 2 to (b,7,7,d) 3 ... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Loss FunctionsThe loss functions used are an important part of this model as it describes our goal when training and how to perform gradient descent. The discriminator loss function is the regular adversarial loss L_adv used in a GAN architecture. But furthermore we have three loss functions added.For this loss functi... | def moving_average(model, model_test, beta=0.999):
for i in range(len(model.weights)):
model_test.weights[i] = (1-beta)*model.weights[i] + beta*model_test.weights[i]
def adv_loss(logits, target):
assert target in [1, 0]
targets = tf.fill(tf.shape(logits), target)
loss = tf.keras.losses.BinaryCross... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
The ModelHere we introduce the class Solver which is the most important class as this will represent our whole model. It will initiate all of our neural networks as well as train our network. | class Solver(tf.keras.Model):
def __init__(self, args):
super(Solver, self).__init__()
self.args = args
self.step=0
self.nets, self.nets_ema = self.build_model(self.args)
# below setattrs are to make networks be children of Solver, e.g., for self.to(self.device)
for name in self.nets.keys():
... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Data Loading and Preprocessing | (trainX, trainy), (valX, valy) = load_data()
trainX=tf.reshape(trainX, (60000,1,28,28,1))
valX=tf.reshape(valX, (10000,1,28,28,1))
inputs=[]
latent_dim=8
for i in range(6000):
i=i+36000
if i % 2000==1999:
print(i+1)
input={}
input['x_src']=tf.cast(trainX[i],tf.float32)
input['y_src']=int(trainy[i])
n=... | 38000
40000
42000
| MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
ParametersThis dictionary contains the different parameters we use to run the model. | args={'img_size':28,
'style_dim':24,
'latent_dim':16,
'num_domains':10,
'lambda_reg':1,
'lambda_ds':1,
'lambda_sty':10,
'lambda_cyc':10,
'hidden_dim':128,
'resume_iter':0,
'ds_iter':6000,
'total_iters':6000,
'batch_size':8,
'val_batch_size... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Load Model | solv=Solver(args)
solv.build_model(args)
solv.load(96000) | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Training | with tf.device('/device:GPU:0'):
solv.train(inputs, inputs) | Start training...
WARNING:tensorflow:Calling GradientTape.gradient on a persistent tape inside its context is significantly less efficient than calling it outside the context (it causes the gradient ops to be recorded on the tape, leading to increased CPU and memory usage). Only call GradientTape.gradient inside the co... | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
ResultsIn this first cell we show an image where the rows represent a source image and the columns the style they are trying to mimic. We can see in this case that that the image still highly resembles the source image but has obtained some characteristics depending on the style of our reference. In most cases this st... | import matplotlib.pyplot as pyplot
for i in range(4):
pyplot.subplot(5,5,2+i)
pyplot.axis('off')
pyplot.imshow(np.reshape(inputs[i]['x_ref'],[28,28]), cmap='gray_r')
for i in range(4):
pyplot.subplot(5, 5, 5*(i+1) + 1)
pyplot.axis('off')
pyplot.imshow(np.reshape(inputs[i]['x_src'], [28,28]), cmap='gray_r')
for j... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Below we generate random styles and see the output it generates. We notice that it is quite likely the images are distorted in this case, compared to when using the style of an already existing image it seems it would usually have a good quality. | for i in range(5):
pyplot.subplot(5,5,1+i)
pyplot.axis('off')
pyplot.imshow(np.reshape(solv.nets['generator'](inputs[0]['x_src'],tf.random.normal((1,24))).numpy(), [28,28]), cmap='gray_r') | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
Here we can see the process of how the image transforms into the target. In these small images there is not too much that is changing but we can still appreciate the process. | s1=solv.nets['style_encoder'](inputs[3]['x_src'],inputs[3]['y_src'])
s2=solv.nets['style_encoder'](inputs[3]['x_ref'],inputs[3]['y_ref'])
for i in range(5):
pyplot.subplot(5,5,1+i)
pyplot.axis('off')
s=(1-i/5)*s1+i/5*s2
pyplot.imshow(np.reshape(solv.nets['generator'](inputs[3]['x_src'],s).numpy(), [28,28]), cma... | _____no_output_____ | MIT | AI-data-Projects/MNIST_GAN/MNIST_Style_GAN_v2.ipynb | nk555/AI-Projects |
git-bakup | USER='tonybutzer'
API_TOKEN='ATOKEN'
GIT_API_URL='https://api.github.com'
def get_api(url):
try:
request = urllib2.Request(GIT_API_URL + url)
base64string = base64.encodestring('%s/token:%s' % (USER, API_TOKEN)).replace('\n', '')
request.add_header("Authorization", "Basic %s" % base64string... | active-fire
| MIT | Attic/repo/git-bakup.ipynb | tonybutzer/etscrum |
Euler Problem 94================It is easily proved that no equilateral triangle exists with integral length sides and integral area. However, the almost equilateral triangle 5-5-6 has an area of 12 square units.We shall define an almost equilateral triangle to be a triangle for which two sides are equal and the third ... | a, b, p, s = 1, 0, 0, 0
while p <= 10**9:
s += p
a, b = 2*a + 3*b, a + 2*b
p = 4*a*a
a, b, p = 1, 1, 0
while p <= 10**9:
s += p
a, b = 2*a + 3*b, a + 2*b
p = 2*a*a
print(s)
| 518408346
| MIT | Euler 094 - Almost equilateral triangles.ipynb | Radcliffe/project-euler |
Now You Code 4: Temperature ConversionWrite a python program which will convert temperatures from Celcius to Fahrenheight.The program should take a temperature in degrees Celcius as input and output a temperature in degrees Fahrenheight.Example:```Enter the temperature in Celcius: 100100 Celcius is 212 Fahrenheight```... | celcius = float(input("enter the temperature in celcius: "))
fahrenhieght=(celcius*9/5)+32
print("fahrenhieght equals " "%.2f" %fahrenhieght) | enter the temperature in celcius: 100
fahrenhieght equals 212.00
| MIT | content/lessons/03/Now-You-Code/NYC4-Temperature-Conversion.ipynb | MahopacHS/spring2019-Christian64Aguilar |
Cross Validation | from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import StratifiedKFold
import numpy as np
seed = 7
np.random.seed(seed)
dataset = np.loadtxt('pima-indians-diabetes.data', delimiter=',')
X = dataset[:, 0:8]
Y = dataset[:, 8]
X.shape
kfold = StratifiedKFold(n_splits=5, shuf... | _____no_output_____ | MIT | keras/170605-cross-validation.ipynb | aidiary/notebooks |
Convolutional Neural Networks with Keras In this lab, we will learn how to use the Keras library to build convolutional neural networks. We will also use the popular MNIST dataset and we will compare our results to using a conventional neural network. Convolutional Neural Networks with KerasObjective for this Notebook... | import tensorflow.keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical | _____no_output_____ | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
When working with convolutional neural networks in particular, we will need additional packages. | from tensorflow.keras.layers import Conv2D # to add convolutional layers
from tensorflow.keras.layers import MaxPooling2D # to add pooling layers
from tensorflow.keras.layers import Flatten # to flatten data for fully connected layers | _____no_output_____ | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
Convolutional Layer with One set of convolutional and pooling layers | # import data
from tensorflow.keras.datasets import mnist
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') | _____no_output_____ | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
Let's normalize the pixel values to be between 0 and 1 | X_train = X_train / 255 # normalize training data
X_test = X_test / 255 # normalize test data | _____no_output_____ | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
Next, let's convert the target variable into binary categories | y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
num_classes = y_test.shape[1] # number of categories | _____no_output_____ | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
Next, let's define a function that creates our model. Let's start with one set of convolutional and pooling layers. | def convolutional_model():
# create model
model = Sequential()
model.add(Conv2D(16, (5, 5), strides=(1, 1), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add... | _____no_output_____ | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
Finally, let's call the function to create the model, and then let's train it and evaluate it. | # build the model
model = convolutional_model()
# fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)
# evaluate the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: {} \n Error: {}".format(scores[1], 100-scores[1]*100)) | WARNING:tensorflow:From /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance wit... | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
* * * Convolutional Layer with two sets of convolutional and pooling layers Let's redefine our convolutional model so that it has two convolutional and pooling layers instead of just one layer of each. | def convolutional_model():
# create model
model = Sequential()
model.add(Conv2D(16, (5, 5), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(8, (2, 2), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), st... | _____no_output_____ | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
Now, let's call the function to create our new convolutional neural network, and then let's train it and evaluate it. | # build the model
model = convolutional_model()
# fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)
# evaluate the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: {} \n Error: {}".format(scores[1], 100-scores[1]*100)) | Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 - 47s - loss: 0.4901 - acc: 0.8633 - val_loss: 0.1385 - val_acc: 0.9570
Epoch 2/10
60000/60000 - 47s - loss: 0.1185 - acc: 0.9642 - val_loss: 0.0848 - val_acc: 0.9728
Epoch 3/10
60000/60000 - 47s - loss: 0.0831 - acc: 0.9740 - val_loss: 0.0633 - v... | MIT | 2. Intro to Deep Learning & Neural Networks with Keras/4. Convolutional Neural Learning/Convolutional-Neural-Networks-with-Keras-py-v1.0.ipynb | aqafridi/AI-Engineering-Specialization |
多元函数微分法及其应用只有一个自变量的函数叫做一元函数。在很多实际问题中往往牵涉到多方面的因素，反映到数学上，就是一个变量依赖于多个变量的情形。这就提出了多元函数以及多元函数的微分和积分问题。本章将在一元函数微分学的基础上，讨论多元函数的微分法及其应用。讨论中我们以二元函数为主，因为从一元函数到二元函数会产生新的问题，而从二元函数到二元以上的多元函数则可以类推。本节包括以下内容：1. 多元函数的基本概念2. 偏导数3. 全微分4. 多元复合函数的求导法则5. 隐函数的求导公式6. 多元函数微分学的几何应用7. 方向导数和梯度8. 多元函数的极值及其求法9. 二元函数的泰勒公式10. 最小二乘法 1. 多元函数的基本概念 1.1 ... | import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
@np.vectorize
def f(x, y):
return x * y / (x ** 2 + y ** 2)
step = 0.05
x_min, x_max = -1, 1
y_min, y_max = -1, 1
x_range, y_range = np.arange(x_min, x_max + step, step), np.ara... | _____no_output_____ | MIT | Multivariable Differential Calculus and its Application.ipynb | reata/Calculus |
重点考察 $(0, 0)$ 这个点：**极限**显然当点 $P(x, y)$ 沿 $x$ 轴趋于点 $(0,0)$ 时$$ \lim_{\begin{split}(x,y)\rightarrow (0,0) \\ y=0 \end{split}}f(x,y) = \lim_{x \rightarrow 0}f(x,0) =\lim_{x \rightarrow 0}0 = 0$$又当点 $P(x, y)$ 沿 $y$ 轴趋于点 $(0,0)$ 时$$ \lim_{\begin{split}(x,y)\rightarrow (0,0) \\ x=0 \end{split}}f(x,y) = \lim_{y \rightarrow 0}... | @np.vectorize
def f(x, y):
return x * y / np.sqrt(x ** 2 + y ** 2)
step = 0.05
x_min, x_max = -1, 1
y_min, y_max = -1, 1
x_range, y_range = np.arange(x_min, x_max + step, step), np.arange(y_min, y_max + step, step)
x_mat, y_mat = np.meshgrid(x_range, y_range)
z = f(x_mat.reshape(-1), y_mat.reshape(-1)).reshape(x_m... | _____no_output_____ | MIT | Multivariable Differential Calculus and its Application.ipynb | reata/Calculus |
Data Import and Check | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection impo... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
* I import data and drop duplicates* I had tried to set the user id as index. Expectedly, it did not work as a user can have multiple trips. However the user - trip combination did not work either which revealed the entire rows duplicated* Once the duplicates are removed, the count of use -trip combinations reveal they... | hoppi = pd.read_csv('C:/Users/gurkaali/Documents/Info/Ben/Hop/WatchesTable.csv', sep=",")
hoppi.drop_duplicates(inplace = True)
hoppi.groupby(['user_id', 'trip_id'])['user_id']\
.count() \
.reset_index(name='count')\
.sort_values(['count'], ascending = False)\
.head(5) | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Now that I am sure, I can set the index: | hoppi.set_index(['user_id', 'trip_id'], inplace = True) | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Pandas has great features for date calculations. I set the related field types as datetime in case I need those features | hoppi['departure_date'] = pd.to_datetime(hoppi['departure_date'], format = '%m/%d/%y')
hoppi['return_date'] = pd.to_datetime(hoppi['return_date'], format = '%m/%d/%y')
hoppi['first_search_dt'] = pd.to_datetime(hoppi['first_search_dt'], format = '%m/%d/%y %H:%M')
hoppi['watch_added_dt'] = pd.to_datetime(hoppi['watch_add... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
The explanations in the assignment do not cover all fields but field names and the content enable further data verification* Stay should be the difference between departure and return dates. Based on that assumption, the query below should return no records i.e. the 1st item in the tuple returned by shape should be 0: | hoppi['stay2'] = pd.to_timedelta(hoppi['stay'], unit = 'D')
hoppi['stay_check'] = hoppi['return_date'] - hoppi['departure_date']
hoppi.loc[(hoppi['stay_check'] != hoppi['stay2']) & (hoppi['return_date'].isnull() == False), \
['stay2', 'stay_check', 'return_date', 'departure_date']].shape | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
The following date fields must not be before the first search date. Therefore the queries below should reveal no records* watch_added_dt* latest_status_change_dt* first_buy_dt* last_notif_dt* forecast_last_warning_date* forecast_last_danger_date | hoppi.loc[(hoppi['watch_added_dt'] < hoppi['first_search_dt']), ['first_search_dt', 'watch_added_dt']].shape
hoppi.loc[(hoppi['latest_status_change_dt'] < hoppi['first_search_dt']), ['first_search_dt', 'latest_status_change_dt']].shape | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
33 records have a first buy suggestion datetime earlier than the user's first search. | hoppi.loc[(hoppi['first_buy_dt'] < hoppi['first_search_dt']), ['first_search_dt', 'first_buy_dt']].shape | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
While the difference is just minutes in most cases, I don't have an explanation to justify it. Given the limited number of cases, I prefer removing them | hoppi.loc[(hoppi['first_buy_dt'] < hoppi['first_search_dt']), ['first_search_dt', 'first_buy_dt']].head()
hoppi = hoppi.loc[~(hoppi['first_buy_dt'] < hoppi['first_search_dt'])] | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
There are also 2 records where the last notification is done before the user's first search. I remove those as well | hoppi.loc[(hoppi['last_notif_dt'] < hoppi['first_search_dt']), ['first_search_dt', 'last_notif_dt']]
hoppi = hoppi.loc[~(hoppi['last_notif_dt'] < hoppi['first_search_dt'])] | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Same checks on last warning and last danger dates show 362K + and 98K + suspicious records. As the quantitiy is large and descriptions sent with the assignment do not contain details on these 2 fields, I prefer to keep them while taking a note here in case something provides with additional argument to delete them duri... | hoppi.loc[(hoppi['forecast_last_warning_date'] < hoppi['first_search_dt']), \
['first_search_dt', 'forecast_last_warning_date']].shape
hoppi.loc[(hoppi['forecast_last_danger_date'] < hoppi['first_search_dt']), \
['first_search_dt', 'forecast_last_danger_date']].shape | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Check outliers I reshape the columns in a way that will make working with seaborn easier: | hoppi_box_components = [hoppi[['first_advance']].assign(measurement_type = 'first_advance').reset_index(). \
rename(columns = {'first_advance': 'measurement'}),
hoppi[['watch_advance']].assign(measurement_type = 'watch_advance').reset_index(). \
re... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
While several observations look like outliers on the boxplots, the histograms below show that the data is highly skewed. Therefore I do not consider them as outliers | f, axes = plt.subplots(1, 3, figsize=(15, 5), sharex=True)
sns.distplot(hoppi['first_advance'], kde=False, color="#FA6866", ax=axes[0])
sns.distplot(hoppi.loc[hoppi['watch_advance'].isnull() == False, 'watch_advance'], kde=False, color="#01AAE4", ax=axes[1])
sns.distplot(hoppi.loc[hoppi['current_advance'].isnull() == F... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Question 1 Given the business model of Hopper, we should understand who is more likely to buy a ticket eventually. Logistic Regression constitutes a convenient way of conducting such analysis. It runs faster than SVN and is easier to interpret, making it ideal for a task like this one: I prepare categorical variables ... | one_hot_trip_type = pd.get_dummies(hoppi['trip_type'])
hoppi2 = hoppi.join(one_hot_trip_type) | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
I believe the city / airport distinction in origin and destination fields refer to the fact that some airports are more central such as the difference between Toronto Billy Bishop and Pearson airports. I also checked some airport codes, they do corresponds to cities where there are multiple airports with one or more be... | origin_cols = hoppi2['origin'].str.split("/", n = 1, expand = True)
hoppi2['origin_code'] = origin_cols[1]
hoppi2['origin_type'] = origin_cols[0]
destination_cols = hoppi2['destination'].str.split("/", n = 1, expand = True)
hoppi2['destination_code'] = destination_cols[1]
hoppi2['destination_type'] = destination_col... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
I prepare categorical variables for whether a watch is placed or not: | hoppi4.loc[hoppi3['watch_added_dt'].isnull() == True, 'watch_bin'] = 0
hoppi4.loc[hoppi3['watch_added_dt'].isnull() == False, 'watch_bin'] = 1 | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Given the user - trip combination being unique across the data file, we do not have information on the changes for a user who has updated his trip status. As the data looks like covering the last status of a trip, I prefer to focus analyses on concluded queries i.e. trips either expired or booked. I exclude:* actives: ... | hoppi4.loc[hoppi3['status_latest'] == 'expired', 'result'] = 0
hoppi4.loc[hoppi3['status_latest'] == 'booked', 'result'] = 1 | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
A person might be prompted to buy once the price falls because it makes sense or maybe he buys as soon as it starts increasing to avoid further increase. Whatever the case, it makes sense to compare the price at different time points with respect to the original price at first search. For that, I create columns to meas... | hoppi4['dif_last_first'] = hoppi4['last_total'] - hoppi4['first_total']
hoppi4['dif_buy_first'] = hoppi4['first_buy_total'] - hoppi4['first_total']
hoppi4['dif_lowest_first'] = hoppi4['lowest_total'] - hoppi4['first_total'] | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
I create a categorical variable for the last recommendation as well to check whether a buy recommendation makes user to book: | one_hot_last_rec = pd.get_dummies(hoppi4['last_rec']) # this create s 2 columns: buy and wait
hoppi5 = hoppi4.join(one_hot_last_rec)
hoppi5.loc[hoppi5['last_rec'].isnull(), 'buy'] = np.nan # originally null values are given 0. I undo that manipulation here | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
I make a table with rows containing certain results that I want to focus on i.e. expired and booked | hoppi6 = hoppi5.loc[hoppi5['result'].isnull() == False,
['round_trip',
'destination_city', 'origin_city',
'weekend',
'filter_no_lcc', 'filter_non_stop', 'filter_short_layover', 'status_updates',
'watch_bin', 'total_notifs', 'total_buy_notifs', 'buy',
'dif_last_first', ... | <class 'pandas.core.frame.DataFrame'>
MultiIndex: 45237 entries, (e42e7c15cde08c19905ee12200fad7cb5af36d1fe3a3310b5f94f95c47ae51cd, 05d59806e67fa9a5b2747bc1b24842189bba0c45e49d3714549fc5df9838ed20) to (d414b1c72a16512dbd7b3859c9c9f574633578acef74d120490625d9010103c7, 3a363a2456b6b7605347e06d2879162b3008004370f73a68f525... | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Some rows have null values such as the price difference between the buy moment and the first price as some users may not have gt the buy recommendation yet. To cover these features, I get only non-null rows: | df = hoppi6.dropna()
df.info()
X = df[['round_trip',
'destination_city', 'origin_city',
'weekend',
'filter_non_stop', 'filter_short_layover', 'status_updates', 'filter_no_lcc',
'watch_bin', 'total_notifs', 'buy', 'total_buy_notifs',
'dif_lowest_first',
'dif_last_first', ... | precision recall f1-score support
0.0 0.99 1.00 0.99 32564
1.0 0.97 0.87 0.92 2743
micro avg 0.99 0.99 0.99 35307
macro avg 0.98 0.93 0.96 35307
weighted avg 0.99 0.99 0.99 ... | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Data driven insights:The model shows a good level of accuracy. However given the imbalance of data (only 8% of data corresponds to an actual booking) it is crucial to check recall which also shows a high value i.e. false negatives are limited.Now that we know the model looks robust, we can make the following data-driv... | hoppi5.loc[(hoppi5['watch_bin'] == 1.0) & (hoppi5['result'] == 0)].info()
pareto_watch_0 = hoppi5.loc[(hoppi5['watch_bin'] == 1.0) & (hoppi5['result'] == 0.0), ['origin_code', 'destination_code']]
pareto_watch_0.loc[pareto_watch_0['origin_code'] < pareto_watch_0['destination_code'], \
'itinerary'] = ... | All observations where the user watched the price but did not book, cover 11697 itineraries. Out of these, 4236 constitute 80% of the whole observation set. That is around 36.2 % of the whole set.
| MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
The list gives the biggest airports. This result reassures that it is additionally critical to make reliable estimations for these itineraries. The top 10 itineraries consist only of US destinations showing the importance of the US market. As we have seen in the previous question, a user setting the watch on is a good ... | dfw = hoppi5.loc[hoppi5['result'].isnull() == False, ['first_advance', 'first_total', 'watch_bin', 'result']]
dfw = dfw.dropna()
dfw.groupby('watch_bin').agg({'first_advance': np.mean, 'first_total': np.mean}) | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
* As the data is skewed using non-parametrical tests makes more sense. I use the Mann Whitney test for that purpose* The test reveal significant difference between watched and non-watched itineraries at 0.1 level in terms of the number of days between the departure and the first search. Those who place a watch have a w... | stat, p = mannwhitneyu(dfw.loc[dfw['watch_bin'] == 1, 'first_advance'],
dfw.loc[dfw['watch_bin'] == 0, 'first_advance'])
print('Statistics=%.3f, p=%.3f' % (stat, p)) | Statistics=41274130.000, p=0.074
| MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
* The test on same user groups (those watching vs those who don't) show that they differ in terms of the price they get at their first search. The difference is highly significant given the p-value. * Those who watch have a trip cost of USD125 more on average.* There might be a growth opportunity in budget passengers. ... | stat, p = mannwhitneyu(dfw.loc[dfw['watch_bin'] == 1, 'first_total'],
dfw.loc[dfw['watch_bin'] == 0, 'first_total'])
print('Statistics=%.3f, p=%.3f' % (stat, p)) | Statistics=29810391.000, p=0.000
| MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Question 3 Chart 1: What is the situation as of now compared to PY?* Note that from the current advance field in the data, I see that we are on April 10th 2018* Expired: Watch is on + Current Date > Departure Date* Inactive: Watch is off + Current Date can be before or after Current Date* Active: Watch is on + Current... | date_range = pd.date_range(start='1/1/2018', end='04/10/2018', freq='D')
df_date = pd.DataFrame(date_range, columns = ['date_range'])
df_date.set_index('date_range', inplace = True) | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
incoming traffic counts the number of first time searches each day: | hoppi5['first_search_dt_dateonly'] = hoppi5['first_search_dt'].dt.date
incoming_traffic = hoppi5.groupby(['first_search_dt_dateonly']) \
.size().reset_index() \
.rename(columns = {0: 'count'})
incoming_traffic.set_index('first_search_dt_dateonly', inplace = True) | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
outgoing traffic counts the number of trips with departure within the same day, each day. Until a trip is considered 'outgoing' there is a chance that it can be converted to booking: | outgoing_traffic = hoppi5.groupby(['departure_date']) \
.size().reset_index() \
.rename(columns = {0: 'count'})
outgoing_traffic.set_index('departure_date', inplace = True) | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
converted traffic is the numbe rof bookings that took place each day i.e. conversions: | hoppi5['latest_status_change_dt_dateonly'] = hoppi5['first_search_dt'].dt.date
converted_traffic = hoppi5.loc[hoppi5['status_latest'] == 'booked'].groupby(['latest_status_change_dt_dateonly']) \
.size().reset_index() \
.rename(columns = {0: 'count'})
converted_traffic.set_index('latest_... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
I join counts on the date range index created above: | df_chart1 = pd.merge(df_date, incoming_traffic, left_index = True, right_index = True, how='left')
df_chart1.rename(columns = {'count': 'incoming_count'}, inplace = True)
df_chart2 = pd.merge(df_chart1, outgoing_traffic, left_index = True, right_index = True, how='left')
df_chart2.rename(columns = {'count': 'outgoing_c... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
I plot the chart here below. Note that the data collection seems to have started as of 2018 start. Therefore the outgoing count do not reflect the reality in the early periods of the chart. Also the number of trips whose departure is in the future at a given time could be shown as well. That would show the pool of trip... | sns.set_style('dark')
fig, ax1 = plt.subplots(figsize=(15,10))
ax2 = ax1.twinx()
sns.lineplot(x=df_chart3['day'],
y=df_chart3['incoming_count'],
color='#6FC28B',
marker = "X",
ax=ax1)
sns.lineplot(x=df_chart3['day'],
y=df_chart3['outgoing_count'],
... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Chart 2: KPIs Affecting Conversion - Categorical KPIs Categorical variables that turned out to have an impact on conversion are worth following daily. As I sugegsted for the 1st chart, it makes more sense to compare these with prior year same period figures.In this chart we follow the % of people who* look for a round... | df_chart_perc1 = hoppi5.loc[hoppi5['departure_date'] >= '04-10-2018'].describe() # describe() gives the mean per vcategory.
# As they were binary, it gives the %
df_chart_perc2 = df_chart_perc1.loc[['mean'], ['round_trip', 'weekend', 'filter_short_layover', 'watch_bin', 'buy']]
df_chart_perc2 = df_chart_perc2.transpos... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Chart 3: KPIs Affecting Conversion - Ordinal KPIs In a similar vein to the Chart2, I look at KPIs having an impact on conversion here as well. This time I check ordinal variables. Again, it would make more sense to compare with prior year same period figures.In this chart we follow * average difference between the low... | df_chart_abs1 = hoppi5.loc[hoppi5['departure_date'] >= '04-10-2018'].describe()
df_chart_abs2 = df_chart_abs1.loc[['mean'], ['dif_lowest_first',
'dif_last_first', 'dif_buy_first',
'first_advance']]
df_chart_abs3 = df_chart_abs2.tra... | _____no_output_____ | MIT | Watch Bookings/Watches Table Analytics Exercise.ipynb | nediyonbe/Data-Challenge |
Background This project deals with artificial advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. This dataset can be explored to train a model that can predict whether or not the new users will click on an ad based on their various low-level features.This data set c... | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style('white')
df_ad = pd.read_csv('Data/advertising.csv')
df_ad.head(3)
df_ad.info()
df_ad.isnull().any()
df_ad.describe() | _____no_output_____ | MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
EDA Age distribution of the dataset | sns.set_context('notebook',font_scale=1.5)
sns.distplot(df_ad.Age,bins=30,kde=False,color='red')
plt.show() | _____no_output_____ | MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
pairplot of dataset defined by `Clicked on Ad` | import warnings
warnings.filterwarnings('ignore') #### since the target variable is numeric, the joint plot by the target variable generates the warning.
sns.pairplot(df_ad,hue='Clicked on Ad') | _____no_output_____ | MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
Model training: Basic Logistic Regression | from sklearn.model_selection import train_test_split
X = df_ad[['Daily Time Spent on Site', 'Age', 'Area Income',
'Daily Internet Usage', 'Male']]
y = df_ad['Clicked on Ad']
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=100) | _____no_output_____ | MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
training | from sklearn.linear_model import LogisticRegression
lr = LogisticRegression().fit(X_train,y_train) | _____no_output_____ | MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
Predictions and Evaluations | from sklearn.metrics import classification_report,confusion_matrix
y_predict = lr.predict(X_test)
pd.DataFrame(confusion_matrix(y_test,y_predict),index=['True 0','True 1'],
columns=['Predicted 0','Predicted 1'])
print(classification_report(y_test,y_predict)) | precision recall f1-score support
0 0.86 0.92 0.89 119
1 0.93 0.86 0.89 131
micro avg 0.89 0.89 0.89 250
macro avg 0.89 0.89 0.89 250
weighted avg 0.89 0.89 0.89 ... | MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
Model training: Optimized Logistic Regression | from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
scaler = StandardScaler().fit(X_train)
X_train_scaled = scaler.transform(X_train)
x_test_scaled = scaler.transform(X_test) | _____no_output_____ | MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
3-fold CV grid search | grid_param = {'C':[0.01,0.03,0.1,0.3,1,3,10]}
grid_lr = GridSearchCV(LogisticRegression(),grid_param,cv=3).fit(X_train_scaled,y_train)
print('best regularization parameter: {}'.format(grid_lr.best_params_))
print('best CV score: {}'.format(grid_lr.best_score_.round(3))) | best regularization parameter: {'C': 0.3}
best CV score: 0.971
| MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
Predictions and Evaluations | y_predict_2 = grid_lr.predict(x_test_scaled)
pd.DataFrame(confusion_matrix(y_test,y_predict_2),index=['True 0','True 1'],
columns=['Predicted 0','Predicted 1'])
print(classification_report(y_test,y_predict_2)) | precision recall f1-score support
0 0.94 1.00 0.97 119
1 1.00 0.94 0.97 131
micro avg 0.97 0.97 0.97 250
macro avg 0.97 0.97 0.97 250
weighted avg 0.97 0.97 0.97 ... | MIT | Mini capstone projects/Ad click prediction_Logistic Regression.ipynb | sungsujaing/DataScience_MachineLearning_Portfolio |
Demo Notebook: The Continuous-Function Estimator Tophat and Spline bases on a periodic boxHello! In this notebook we'll show you how to use the continuous-function estimator to estimate the 2-point correlation function (2pcf) with a method that produces, well, continuous correlation functions. Load in data We'll demo... | x, y, z = read_lognormal_catalog(n='3e-4')
boxsize = 750.0
nd = len(x)
print("Number of data points:",nd) | Number of data points: 125342
| MIT | example_theory.ipynb | abbyw24/Corrfunc |
We'll also want a random catalog, that's a bit bigger than our data: | nr = 3*nd
x_rand = np.random.uniform(0, boxsize, nr)
y_rand = np.random.uniform(0, boxsize, nr)
z_rand = np.random.uniform(0, boxsize, nr)
print("Number of random points:",nr)
print(x)
print(x_rand) | [1.13136184e+00 4.30035293e-01 2.08324015e-01 ... 7.49666077e+02
7.49922791e+02 7.49938477e+02]
[567.62600303 166.85340522 461.79238824 ... 577.65066275 9.85155819
581.1525008 ]
| MIT | example_theory.ipynb | abbyw24/Corrfunc |
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