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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Using `tables_io.read`, `tables_io.write` and `tables_io.convert`These functions can be used to read and write single tables and to convert them to different formatsThe Tables can be in any of the formats that `tables_io` supports, see more on that in the notebook below.Let's have a look | # Standard imports
import os
import numpy as np
import tables_io
from tables_io.testUtils import make_test_data
# make several tables and grab one
tables = make_test_data()
data = tables['data']
data
data_np = tables_io.convert(data, tables_io.types.NUMPY_DICT)
data_np
data_pd = tables_io.convert(data, tables_io.types.... | _____no_output_____ | MIT | nb/singleTable_example.ipynb | LSSTDESC/tables_io |
File IO with `tables_io`We can write tables into several different formats. These include:1. fits: Writing `astropy.table.Table` objects to FITS files (with the suffix 'fits')2. hf5: Writing `astropy.table.Table` objects to HDF5 files (with the suffix 'hf5')3. hfd5: Writing `numpy.array` objects to HDF5 files (with ... | all_fmts = list(tables_io.types.FILE_FORMAT_SUFFIXS.keys()) + [None]
print(all_fmts) | _____no_output_____ | MIT | nb/singleTable_example.ipynb | LSSTDESC/tables_io |
Ok let's write the data to different files | for fmt in all_fmts:
if fmt is None:
basename = 'test_single_native'
else:
basename = 'test_single_out'
print("Writing to %s using format %s" % (basename, fmt))
try:
os.unlink('%s.%s' % (basename, fmt))
except:
pass
try:
tables_io.write(data, basename, fmt... | _____no_output_____ | MIT | nb/singleTable_example.ipynb | LSSTDESC/tables_io |
Ok, now let's read things back | data_r_fits = tables_io.read("test_single_out.fits")
data_r_fits
data_r_hdf5 = tables_io.read("test_single_out.hdf5")
data_r_hdf5
data_r_hf5 = tables_io.read("test_single_out.hf5")
data_r_hf5
data_r_pq = tables_io.read("test_single_out.pq", keys=[''])
data_r_pq
data_r_h5 = tables_io.read("test_single_out.h5")
data_r_h5... | _____no_output_____ | MIT | nb/singleTable_example.ipynb | LSSTDESC/tables_io |
from time_space_reductions.netcdf_gdf_setter import Base_class_space_time_netcdf_gdfclass Space_Time_Agg_over_polygons(Base_class_space_time_netcdf_gdf): def __init__(self, gdf, xarray_dataset=None, netcdf_temporal_coord_name='time', geo_series_temporal_attribute_name = 'Datetime... | gdf = make_fake_data(3)
gdf.geometry = gdf.geometry.buffer(1.15) # in degrees
xnetcdf = get_netcdf_example()
| C:\Anaconda3\envs\Python_3.8\lib\site-packages\pyproj\crs\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-chan... | MIT | examples/.ipynb_checkpoints/example_Polygons_reduction-checkpoint.ipynb | PhilipeRLeal/time_space_reductions |
Using the algorithm | xnetcdf['new_data'] = xnetcdf['adg_443_qaa'] * 5 - 15
Match_Upper = get_zonal_match_up(gdf=gdf,
netcdf=xnetcdf,
netcdf_varnames =['adg_443_qaa', 'new_data'],
dict_of_windows=dict(time_window='1M'),
agg_functi... | _____no_output_____ | MIT | examples/.ipynb_checkpoints/example_Polygons_reduction-checkpoint.ipynb | PhilipeRLeal/time_space_reductions |
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from itertools import product
import re
import time
from sklearn.model_selection import train_test_split
from sklearn.manifold import TSNE
import tensorflow as tf
from tensorflow import keras | _____no_output_____ | MIT | nlp/v1_scripts/supervised_UCI_adam256_save_embedding.ipynb | solislemuslab/dna-nn-theory | |
Read Data | !pip install PyDrive
from google.colab import drive
drive.mount('/content/gdrive')
def convert_label(row):
if row["Classes"] == 'EI':
return 0
if row["Classes"] == 'IE':
return 1
if row["Classes"] == 'N':
return 2
data_path = '/content/gdrive/My Drive/Colab Notebooks/UCI/'
splice_df = pd.read_csv(data... | Training Dataset: loss = 0.336 and acccuracy = 86.82%
Validation Dataset: loss = 0.556 and acccuracy = 78.28%
Test Dataset: loss = 0.508 and acccuracy = 80.25%
| MIT | nlp/v1_scripts/supervised_UCI_adam256_save_embedding.ipynb | solislemuslab/dna-nn-theory |
Plaid Access TokenIn this section, you will use the plaid-python api to generate the correct authentication tokens to access data in the free developer Sandbox. This mimics how you might connect to your own account or a customer account, but due to privacy issues, this homework will only require connecting to and anal... | INSTITUTION_ID = "ins_109508"
client = plaid.Client(client_id=PLAID_CLIENT_ID,
secret=PLAID_SBX_SECRET_KEY,
public_key=PLAID_PUBLIC_KEY,
environment= PLAID_ENV ) | _____no_output_____ | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
2. Generate a public token | generate_tkn_response = client.Sandbox.public_token.create(INSTITUTION_ID,['transactions','income', 'assets'])
generate_tkn_response | _____no_output_____ | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
3. Exchange the public token for an access token | exchange_tkn_response = client.Item.public_token.exchange(generate_tkn_response['public_token'])
access_token = exchange_tkn_response['access_token'] | _____no_output_____ | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
4. Fetch Accounts | client.Accounts.get(access_token) | _____no_output_____ | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
--- Account Transactions with PlaidIn this section, you will use the Plaid Python SDK to connect to the Developer Sandbox account and grab a list of transactions. You will need to complete the following steps:1. Use the access token to fetch the transactions for the last 90 days2. Print the categories for each transac... | start_date = '{:%Y-%m-%d}'.format(datetime.datetime.now() + datetime.timedelta(-90))
end_date = '{:%Y-%m-%d}'.format(datetime.datetime.now())
transaction_response = client.Transactions.get(access_token,start_date,end_date)
pretty_print_response(transaction_response['transactions'][:1]) | [
{
"account_id": "dQA7rAZzR8HMMERWxxXwIl6k46WEvPfZzzk7A",
"account_owner": null,
"amount": 25,
"authorized_date": null,
"category": [
"Payment",
"Credit Card"
],
"category_id": "16001000",
"date": "2020-07-14",
"iso_cur... | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
2. Print the categories for each transaction | for transaction in transaction_response['transactions']:
print(transaction['category']) | ['Payment', 'Credit Card']
['Travel', 'Taxi']
['Transfer', 'Debit']
['Transfer', 'Deposit']
['Recreation', 'Gyms and Fitness Centers']
['Travel', 'Airlines and Aviation Services']
['Food and Drink', 'Restaurants', 'Fast Food']
['Food and Drink', 'Restaurants', 'Coffee Shop']
['Food and Drink', 'Restaurants']
['Transfer... | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
3. Create a new DataFrame using the following fields from the JSON transaction data: date, name, amount, category. (For categories with more than one label, just use the first category label in the list) | transaction_df = pd.DataFrame(columns=['Date','Name','Amount','Category'])
date = []
name = []
amount = []
category = []
for i in transactions:
date.append(i['date'])
name.append(i['name'])
amount.append(i['amount'])
category.append(i['category'][0])
transaction_df['Date'] = date
transaction_d... | _____no_output_____ | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
4. Convert the data types to the appropriate types (i.e. datetimeindex for the date and float for the amount) | transaction_df.dtypes
transaction_df['Date'] = pd.to_datetime(transaction_df['Date'])
transaction_df = transaction_df.set_index(['Date'])
transaction_df.dtypes | _____no_output_____ | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
--- Income Analysis with PlaidIn this section, you will use the Plaid Sandbox to complete the following:1. Determine the previous year's gross income and print the results2. Determine the current monthly income and print the results3. Determine the projected yearly income and print the results | income_response = client.Income.get(access_token)
pretty_print_response(income_response)
print(f"Last Year's income: {income_response['income']['last_year_income_before_tax']}")
print(f"Current monthly income: {income_response['income']['income_streams'][0]['monthly_income']}")
print(f"Projected Year's income: {income_... | Projected Year's income: 7389
| Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
--- Budget AnalysisIn this section, you will use the transactions DataFrame to analyze the customer's budget1. Calculate the total spending per category and print the results (Hint: groupby or count transactions per category)2. Generate a bar chart with the number of transactions for each category 3. Calculate the exp... | # YOUR CODE HERE
expenses_by_category = transaction_df.groupby('Category').sum()["Amount"]
expenses_by_category
expenses_by_category.plot(kind = "pie", title = "Expenses by Category",subplots=True, figsize = (10,10))
| _____no_output_____ | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
Calculate the expenses per month | transaction_df.reset_index(inplace=True)
transaction_df['month'] = pd.DatetimeIndex(transaction_df['Date']).month
transaction_df.head()
transactions_per_month = transaction_df.groupby('month').sum()
transactions_per_month
transactions_per_month.plot(kind = 'bar', title = "Transactions per Month", rot=45)
| _____no_output_____ | Apache-2.0 | account_summary.ipynb | mostafajoma/API_Homework |
Environment Checker Utils Utilities for collecting/checking `fastai` user environment | from fastai.utils import *
from fastai.gen_doc.nbdoc import *
from fastai.utils import *
show_doc(show_install) | _____no_output_____ | Apache-2.0 | docs_src/utils.collect_env.ipynb | oegedijk/fastai |
without nvidia-smi printout:`python -m fastai.utils.show_install`with nvidia-smi printout:`python -m fastai.utils.show_install 1` [`show_install`](/utils.collect_env.htmlshow_install) | show_doc(check_perf) | _____no_output_____ | Apache-2.0 | docs_src/utils.collect_env.ipynb | oegedijk/fastai |
Multi-label classification | %reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.conv_learner import *
PATH = 'data/planet/'
# Data preparation steps if you are using Crestle:
os.makedirs('data/planet/models', exist_ok=True)
os.makedirs('/cache/planet/tmp', exist_ok=True)
!ln -s /datasets/kaggle/planet-understanding-the-amazon-fr... | [0m[01;34mmodels[0m/ [01;34mtest-jpg[0m/ [01;34mtmp[0m/ [01;34mtrain-jpg[0m/ [01;32mtrain_v2.csv[0m*
| Apache-2.0 | courses/dl1/lesson2-image_models.ipynb | wpride/fastai |
Multi-label versus single-label classification | from fastai.plots import *
def get_1st(path): return glob(f'{path}/*.*')[0]
dc_path = "data/dogscats/valid/"
list_paths = [get_1st(f"{dc_path}cats"), get_1st(f"{dc_path}dogs")]
plots_from_files(list_paths, titles=["cat", "dog"], maintitle="Single-label classification") | _____no_output_____ | Apache-2.0 | courses/dl1/lesson2-image_models.ipynb | wpride/fastai |
In single-label classification each sample belongs to one class. In the previous example, each image is either a *dog* or a *cat*. | list_paths = [f"{PATH}train-jpg/train_0.jpg", f"{PATH}train-jpg/train_1.jpg"]
titles=["haze primary", "agriculture clear primary water"]
plots_from_files(list_paths, titles=titles, maintitle="Multi-label classification") | _____no_output_____ | Apache-2.0 | courses/dl1/lesson2-image_models.ipynb | wpride/fastai |
In multi-label classification each sample can belong to one or more clases. In the previous example, the first images belongs to two clases: *haze* and *primary*. The second image belongs to four clases: *agriculture*, *clear*, *primary* and *water*. Multi-label models for Planet dataset | from planet import f2
metrics=[f2]
f_model = resnet34
label_csv = f'{PATH}train_v2.csv'
n = len(list(open(label_csv)))-1
val_idxs = get_cv_idxs(n) | _____no_output_____ | Apache-2.0 | courses/dl1/lesson2-image_models.ipynb | wpride/fastai |
We use a different set of data augmentations for this dataset - we also allow vertical flips, since we don't expect vertical orientation of satellite images to change our classifications. | def get_data(sz):
tfms = tfms_from_model(f_model, sz, aug_tfms=transforms_top_down, max_zoom=1.05)
return ImageClassifierData.from_csv(PATH, 'train-jpg', label_csv, tfms=tfms,
suffix='.jpg', val_idxs=val_idxs, test_name='test-jpg')
data = get_data(256)
x,y = next(iter(data.val_dl))
y
list(zi... | _____no_output_____ | Apache-2.0 | courses/dl1/lesson2-image_models.ipynb | wpride/fastai |
sentinelRequestsentinelRequest can be used to colocate a geodataframe (ie areas, trajectories, buoys, etc ...) with sentinel (1, but also 2 , 3 : all known by scihub) Install```conda install -c conda-forge lxml numpy geopandas shapely requests fiona matplotlib jupyter descartespip install --upgrade git+https://github... | !sentinelrequest --help | usage: sentinelrequest [-h] [--user USER] [--password PASSWORD] [--date DATE]
[--wkt WKT] [--filename FILENAME] [--query QUERY]
[--datatake DATATAKE] [--dateformat DATEFORMAT]
[--dtime DTIME] [--cachedir CACHEDIR]
[--cacherefres... | MIT | README.ipynb | agrouaze/sentinelrequest |
"One shot" from command line:`% sentinelrequest --user=xxxx --password=xxxxx --date='2018-09-23 00:00' --date='2018-09-23 12:00' --filename='S1?_?W_GRD*.SAFE' --cachedir=/home1/scratch/oarcher/scihub_cache/ --wkt='POLYGON ((-10 75, -10 86, 12 86, 12 84, -10 75))'````INFO:sentinelRequest:from 2018-09-23 00:00:00 to 201... | %matplotlib inline
import geopandas as gpd
import datetime
import matplotlib.pyplot as plt
import shapely.wkt as wkt
# get your own credential from https://scihub.copernicus.eu/dhus
import pickle
user,password = pickle.load(open("credential.pkl","rb"))
import sentinelrequest as sr
# set default values, so we don't h... | Help on function scihubQuery in module sentinelrequest:
scihubQuery(gdf=None, startdate=None, stopdate=None, date=None, dtime=None, timedelta_slice=None, filename=None, datatake=0, duplicate=False, query=None, user=None, password=None, min_sea_percent=None, fig=None, cachedir=None, cacherefreshrecent=None, progress=Tr... | MIT | README.ipynb | agrouaze/sentinelrequest |
Simplest api usageJust a startdate and a stopdate are given, with no geometry | fig = plt.figure(figsize=(10,7))
safes = sr.scihubQuery(
startdate=datetime.datetime(2018,10,2),
stopdate=datetime.datetime(2018,10,3),
fig=fig) | WARNING:sentinelRequest:Assuming UTC date on col beginposition
WARNING:sentinelRequest:Assuming UTC date on col endposition
| MIT | README.ipynb | agrouaze/sentinelrequest |
The result is a geodataframe with most information from scihub: | safes.iloc[0] | _____no_output_____ | MIT | README.ipynb | agrouaze/sentinelrequest |
Most fields are converted from str to python type (geometry, datetime, int ...) | safes.iloc[1:4]['footprint'].plot()
print('safe was ingested %s after aquisition' % (safes.iloc[0]['ingestiondate']-safes.iloc[0]['endposition'])) | safe was ingested 0 days 05:50:26.096000 after aquisition
| MIT | README.ipynb | agrouaze/sentinelrequest |
Using a geodataframe with geometries As an example, two areas are defined. Note that the index is named with the area name | gdf = gpd.GeoDataFrame({
"beginposition" : [ datetime.datetime(2018,10,2,0) , datetime.datetime(2018,10,13,0) ],
"endposition" : [ datetime.datetime(2018,10,2,21) ,datetime.datetime(2018,10,13,18) ],
"geometry" : [ wkt.loads("POINT (-7.5 53)").buffer(4), wkt.loads("POLYGON ((-12 35, -5... | WARNING:sentinelRequest:Assuming UTC date on col beginposition
WARNING:sentinelRequest:Assuming UTC date on col endposition
WARNING:sentinelRequest:no crs provided. assuming lon/lat with greenwich/antimeridian handling
INFO:sentinelRequest:Slicing into 2 chunks of 7 days, 0:00:00 ...
INFO:sentinelRequest:Slicing done i... | MIT | README.ipynb | agrouaze/sentinelrequest |
User requested area are in green, and found safes are in blue. Index from original request are preserved, so it's easy to know the area that belong to a safe. (See end of example 2 for advanced index handling). | safes.loc['Portugal'] | _____no_output_____ | MIT | README.ipynb | agrouaze/sentinelrequest |
Working with projectionSentinelRequest works with projections, by defining crs in gdf.The colocalisation is done using this crs.get safes around 1000km, at 84° (North pole included) | import pyproj
gdf = gpd.GeoDataFrame({
"beginposition" : [ datetime.datetime(2019,12,1,0) ],
"endposition" : [ datetime.datetime(2019,12,4,0)],
"geometry" : [ wkt.loads("POINT (0 84)")]
},index=["Artic"], crs=pyproj.CRS('epsg:4326'))
# to polar projection (units in meters)
gdf.t... | WARNING:sentinelRequest:Assuming UTC date on col beginposition
WARNING:sentinelRequest:Assuming UTC date on col endposition
| MIT | README.ipynb | agrouaze/sentinelrequest |
Cyclone track colocalization | import pandas as pd
#ibtracs=gpd.read_file('tmp/IBTrACS.NA.list.v04r00.points.shp')
#gdf_track=ibtracs[ibtracs['SID'] == '2019235N10324']
#gdf_track=gdf_track[['ISO_TIME','USA_WIND','geometry']]
#gdf_track['ISO_TIME']=pd.to_datetime(gdf_track['ISO_TIME'],format="%Y-%m-%d %H:%M:%S")
#gdf_track.reset_index(inplace = True... | INFO:sentinelRequest:Slicing into 3 chunks of 7 days, 0:00:00 ...
INFO:sentinelRequest:Slicing done in 0.1s . 3/3 non empty slices.
| MIT | README.ipynb | agrouaze/sentinelrequest |
datatakeHere, `datatake=1` is specified to retrieve adjacents safes from colocated ones (in cyan). When specified, the result contain a `datatake_index` column. 0 means the colocated one, and other values are the range of the adjacent safe (up to -n..n with `datatake=n`)Positive `datatake_index` are for safes *after* ... | safes[['filename','datatake_index']] | _____no_output_____ | MIT | README.ipynb | agrouaze/sentinelrequest |
Time slicing with timedelta_sliceOne can see on previous figure that 3 requests are done. gdf rows are grouped to reduce the amount of scihub requests with the `timedelta_slice` parameter (default to `datetime.timedelta(weeks=1)` )If we reduce `timedelta_slice`, we can see that more scihub request are done, with less ... | # same request as above, but with reduced timedelta_slice
fig = plt.figure(figsize=(10,7))
safes = sr.scihubQuery(
gdf=gdf_track,
date='ISO_TIME',
dtime=datetime.timedelta(hours=1.5),
timedelta_slice=datetime.timedelta(days=1),
datatake=1,
full_fig = True, # to show inte... | INFO:sentinelRequest:Slicing into 18 chunks of 1 day, 0:00:00 ...
INFO:sentinelRequest:Slicing done in 0.1s . 18/18 non empty slices.
| MIT | README.ipynb | agrouaze/sentinelrequest |
Merging source and result with shared indexAs seen before, the result (safes) share the same index as the source. So we can merge the two geodataframe, to associate a wind speed from the cyclone track with the safe, and compute distance from the eye to the safe. | # here, we merge the result with the source request, to associate wind speed to each safe.
merged=safes[['filename','datatake_index','footprint']].merge(
gdf_track[['USA_WIND','geometry']],left_index=True,right_index=True)
merged['eye_dist'] = merged.set_geometry('geometry').distance(merged.set_geometry('footprint'... | <ipython-input-16-6297d856a9af>:4: UserWarning: Geometry is in a geographic CRS. Results from 'distance' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.
merged['eye_dist'] = merged.set_geometry('geometry').distance(merged.set_geometry('footprint').ext... | MIT | README.ipynb | agrouaze/sentinelrequest |
Annexes Antimeridian handling: small geometry vs large oneGiven 2 points on the earth, there is two possible paths: one short, and one long that wrap around the earth.Note: only longitude is wrapped, as if earth was a cylinder (epgs 4326 used for computation) By default, geometry are the smallest ones. To preserve a ... | from shapely.geometry import GeometryCollection
# the polygon is more than 180 deg wide. It will be wrapped, and will cross antimeridian
large_poly = wkt.loads("POLYGON ((-140 -14, 140 -14, 140 -20, -140 -20, -140 -14))")
gdf = gpd.GeoDataFrame({
"beginposition" : [ datetime.datetime(2018,10,1)],
"end... | _____no_output_____ | MIT | README.ipynb | agrouaze/sentinelrequest |
Reproduct Autopilot ArchitectureThe Autopilot has the following Architecture:~ ResNet50-like backbone~ FPN - DeepLabV3- UNet - like heads~ 15 tasks ~ subtasks i.e. if task is car detection, then the sub task is what kind of car, is it stationary? Parked, broken down?For later exploration:Stitching up of images acro... | import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import models, transforms
import matplotlib.pyplot as plt
import numpy as np
batch_size = 50
# get the CIFAR-10 images:
train_data_transform = transforms.Compose([
transforms.Resize(224),
transforms.RandomHorizo... | Files already downloaded and verified
| MIT | Autopilot/Autopilot_replicated.ipynb | Hira63S/DeepLearningResearch |
CIFAR10 Data10000 x 3072 numpy arrays. i.e 1024 values i.e. 32x32 image and since there are 3 channels, we get 3072 shaped numpy arrays.Each row in the array stores a 32x32 colour imageThe first 1024 entries contain the red channel values, the next 1024 are green and the next 1024 are blue. | def imshow(img):
img = img/2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1,2,0)))
plt.show()
dataiter = iter(train_loader)
images, labels = dataiter.next()
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
imshow(torchv... | _____no_output_____ | MIT | Autopilot/Autopilot_replicated.ipynb | Hira63S/DeepLearningResearch |
Now, onto the transfer learning scenario where we are going to use the pretrained network as a feature extractor.1. Let's use ResNet502. Replace last layer of the model with a new layer with 10 outputs3. Exclude the existing network layers from the backward pass and only pass the newly added fully-connected layer to th... | model = torchvision.models.resnet50(pretrained=True)
model.eval()
def train(model, criterion,
optimizer,
train_loader,
valid_loader,
save_file_name,
max_epochs_stop=3,
n_epochs = 20,
print_every=2):
"""Train the Pytorch model while including the ... | _____no_output_____ | MIT | Autopilot/Autopilot_replicated.ipynb | Hira63S/DeepLearningResearch |
model.classifier[6] = nn.Sequential( nn.Linear(n_inputs, 256), nn.ReLU(), nn.Dropout(0.4), nn.Linear(256, n_classes), nn.LogSoftmax(dim=1)) | tl_feature_extractor() | Epoch 1/5
| MIT | Autopilot/Autopilot_replicated.ipynb | Hira63S/DeepLearningResearch |
from google.colab import auth
auth.authenticate_user()
from google.colab import drive
drive.mount('/content/drive') | Mounted at /content/drive
| MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis | |
E-commerce Dataset 2020**For this project, we want to enhance the webpage with the provided dataset of e-Commerce dataset in February and April by taking in consideration COVID-19 pandemic.**Goal: Our goal is analyzing customer online purchasing behavior during pre covid-19 and post covid-19 using February and April da... | %%bigquery --project ba775-team-4b
SELECT brand, count(brand) AS total_brand_purchase
FROM `ba775-team-4b.4b_dataset.ecommerce_feb_2020`
WHERE event_type = 'purchase'
GROUP BY brand
ORDER BY count(brand) DESC
LIMIT 10 | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Results: We noticed that Samsung is consumer’s favorite brand, followed by Apple and Xiaomi. All electronic brands! Next, let’s give a look to the conversion rate for the month of February from 2020. | %%bigquery --project ba775-team-4b
SELECT
COUNTIF(event_type = 'purchase')/COUNT(event_type) AS conversion_rate,
category_code,
ROUND(sum(price), 2) AS total_Price
FROM `ba775-team-4b.4b_dataset.ecommerce_feb_2020`
GROUP BY category_code
ORDER BY Conversion_rate DESC
limit 10 | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Results: It shows that the highest conversion rate is the construction tools with a rate of 0.0397, followed by apparel with a rate of 0.0334. We also want to identify who are the Top consumers in the ecommerce webpage, and how much do they spend for this month, February. | %%bigquery --project ba775-team-4b
SELECT user_id, ROUND(SUM(price)) AS total_spending_FEB
FROM `ba775-team-4b.4b_dataset.ecommerce_feb_2020`
WHERE event_type = 'purchase'
GROUP BY user_id
ORDER BY total_spending_FEB DESC
LIMIT 10 | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Result: our Top consumer identified as user_id: '563051763' spent around $302,726.0 on the webpage. Now, let’s check the average of all users’ spendings for the month of February. | %%bigquery --project ba775-team-4b
SELECT ROUND(AVG(price), 2) as avg_spending_feb
FROM `ba775-team-4b.4b_dataset.ecommerce_feb_2020`
WHERE event_type = 'purchase'
LIMIT 10 | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Result: The average of users’ spendings for the month of February is $317.57. After identifying the highest consumer as user_id: '563051763', we want to compare their spending with the average of all users’ spendings for the month of February. | %%bigquery --project ba775-team-4b
SELECT user_id AS USER_ID, ROUND(SUM(price)) AS Total_spending, COUNT(user_session) as Sessions,
(
SELECT round(AVG(price), 2) as avg_spending
FROM `ba775-team-4b.4b_dataset.ecommerce_feb_2020`
WHERE event_type = 'purchase'
) as Avg_spending_of_all_users
FROM `ba775-team-4b.4b_da... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
This query shows the top buyer who have spend the most in this ecommerce website, number of sessions, and the average of all users' spendings. Now, we would check for the month of April (post-COVID): For the month of April, 2020. We will identify the Top 10 popular brands that were purchased by the consumers during po... | %%bigquery --project ba775-team-4b
SELECT brand, count(brand) AS total_brand_purchase
FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020`
WHERE event_type = 'purchase'
GROUP BY brand
ORDER BY count(brand) DESC
LIMIT 10 | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Results: We noticed that the results are the same as pre-COVID (February). Samsung is still consumer’s favorite brand, followed by Apple and Xiaomi. All electronic brands! Let’s give a look to the conversion rate for the month of April. | %%bigquery --project ba775-team-4b
SELECT countif(event_type = 'purchase')/count(event_type) AS Conversion_rate, category_code, sum(price) Total_Price
FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020`
GROUP BY category_code
ORDER BY Conversion_rate DESC
limit 10 | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Results: It shows that the highest conversion rate is the stationery paper with a rate of 0.038, followed by kitchen appliances with a small difference of 0.001. Next, we want to identify who are the Top consumers in the ecommerce webpage, and how much they spend. | %%bigquery --project ba775-team-4b
SELECT user_id, round(SUM(price), 2) as total_spending
FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020`
WHERE event_type = 'purchase'
GROUP BY user_id
ORDER by total_spending DESC
LIMIT 10 | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Results: our Top consumer identified as user_id: '553446649' spent around $122,525.19 on the webpage, which is way more lower than in February. This leads to a better understanding of consumers' behaviour that due the pandemic consumers had shifted their values, and cutting down unneed purchases to essentials. Now, let... | %%bigquery --project ba775-team-4b
SELECT ROUND(AVG(price), 2) as avg_spending_of_all_users
FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020`
WHERE event_type = 'purchase' | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Result: As we expected, the average of users’ spendings also decreased during pandemic. Average for April is `$252.93`, while for Feb was `$317.57`. After identifying the highest consumer as user_id: '553446649', we want to compare their spending with the average of all users’ spendings for the month of April. | %%bigquery --project ba775-team-4b
SELECT user_id AS USER_ID, ROUND(SUM(price)) AS TOTAL_SPENDING,COUNT(user_session) AS TOTA_SESSIONS,
(
SELECT round(AVG(price), 2) as avg_spending
FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020`
WHERE event_type = 'purchase'
) as AVG_SPENDING_OF_ALL_USERS
FROM `ba775-team-4... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
This query shows the top buyer who have spend the most in this ecommerce website, and the average of all users' spendings Let's check deeper the insights by combining both datasets! We also want to know how many times they shop on this website in February and April. | %%bigquery --project ba775-team-4b
SELECT
user_id,
COUNT(user_id) AS returning
FROM
(
SELECT user_id, event_type
FROM `ba775-team-4b.4b_dataset.ecommerce_feb_2020` AS FEB
UNION ALL
SELECT user_id, event_type
FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020` AS APR
)
WHERE event_type = 'purchase'
GROUP BY user_id
HA... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Results: Our top consumer identified as user_id: ‘609817194’ returned back to the website 573 times. Surprisingly, our top consumer from the month of February and April are not in the Top 10. Now we want to see the impact of COVID-19 by finding the percentage change in sales for popular categories in February | %%bigquery --project ba775-team-4b
SELECT
category_code,
(total_sales_apr - total_sales_feb)/total_sales_feb AS percent_change
FROM
(
SELECT *
FROM
(SELECT
category_code,
SUM(price) AS total_sales_apr
FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020`
WHERE category_code IS NOT NULL AND event_type = 'purchase'... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Results: Most popular categories have lower sales in April than in February. But headphone, massager and refrigerators categories have higher sales in April. It might casued by working from home. Are views correlated with the number of speding in each month? | %%bigquery --project ba775-team-4b
select count(event_type) as feb_visits,
(select round(sum(price),2) as profit_from_feb from `ba775-team-4b.4b_dataset.ecommerce_feb_2020` where event_type = 'purchase')feb_profit,
(select count(event_type) as total_visits_april FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020`
w... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
There is a negative correlation between the number of views and consumer's spending on each month.Results: April visits times increased by 9.8% since February, but decreased sales by 21.84%. Also, we wants to investigate how well samsung's top selling categories in February performed in April | %%bigquery --project ba775-team-4b
SELECT
A.*,FEB_total_sales,round(APR_total_sales-FEB_total_sales,2) FEB_APR_SalesDifferences,
round(((APR_total_sales-FEB_total_sales)/FEB_total_sales),2) FEB_APR_SalesChanges_percentage
FROM
(SELECT A.*,B.APR_total_sales
FROM
(SELECT A.brand,A.category_code,count(A.brand) AS coun... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Result: most of the February's top-selling categories for samsung increased a lot in April, especially for air conditioner category. Number of products they purchased in different category. | %%bigquery --project ba775-team-4b
SELECT user_id,num_of_accessories,num_of_apparel,num_of_appliances,num_of_auto,num_of_computers,num_of_construction,num_of_country_yard,num_of_electronics,num_of_furniture,num_of_kids,num_of_medicine,num_of_sport,num_of_stationery from
(SELECT user_id, count(user_id) as num_of_accesso... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Abandon Rate | %%bigquery --project ba775-team-4b
SELECT a.user_id,
a.cart_count,
ifnull(b.purchase_count,0) as purchase_count,
round((ifnull(b.purchase_count,0)/a.cart_count),3)as purchase_rate,
round((1-(ifnull(b.purchase_count,0)/a.cart_count)),3) as abandon_rate
FROM `ba775-team-4b.4b_dataset.cart_count_per_user_id` a
left j... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
The abandon rate can be used as the metric to evaluate the purchase power of the users. The abandon rate of a certain user = 1-(number of 'purchase' event /number of 'cart' event). Only the user info with high abandon rate(>0.9) is included and have the potential to work on increasing sales conversion, such as sending ... | # Using product features
CREATE MODEL
model.products
OPTIONS
( model_type = 'kmeans',
num_clusters = 2,
distance_type = 'euclidean') AS
SELECT
category_code, brand, price, count(product_id)
FROM `ba775-team-4b.4b_dataset.ecommerce_april_2020`
WHERE event_type='purchase'
GROUP BY category_code, product_id, ... | _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
Tableau Dashboard https://prod-useast-a.online.tableau.com/t/soltaniehha/views/BA775Team4bProject_16040742108920/EcommerceDashboard?:origin=card_share_link&:embed=n | from IPython.display import Image
# Top Purchase Brand of Feberary and April
Image(filename='Top Brand.png')
# Abandon Rate of Feberary and April
Image(filename='Abandon Rate.png')
| _____no_output_____ | MIT | BA_775_Team_Assignment_Team_4b.ipynb | Amberineee/ecommerce_covid_analysis |
define functions | read_FM <- function(filename){
df_FM = data.frame(readRDS(filename),stringsAsFactors=FALSE,check.names=FALSE)
rownames(df_FM) <- make.names(rownames(df_FM), unique=TRUE)
df_FM[is.na(df_FM)] <- 0
return(df_FM)
}
run_pca <- function(mat,num_pcs=50,remove_first_PC=FALSE,scale=FALSE,center=FALSE){
set.... | _____no_output_____ | MIT | Extra/Cusanovich_2018_subset/test_blacklist/run_umap_cusanovich2018subset_no_blacklist_filtering.ipynb | tAndreani/scATAC-benchmarking |
Input | workdir = '../output/'
path_umap = paste0(workdir,'umap_rds/')
system(paste0('mkdir -p ',path_umap))
path_fm = paste0(workdir,'feature_matrices/')
metadata <- read.table('../input/metadata.tsv',
header = TRUE,
stringsAsFactors=FALSE,quote="",row.names=1)
list.files(pa... | _____no_output_____ | MIT | Extra/Cusanovich_2018_subset/test_blacklist/run_umap_cusanovich2018subset_no_blacklist_filtering.ipynb | tAndreani/scATAC-benchmarking |
SnapATAC | df_umap_SnapATAC <- run_umap(fm_SnapATAC)
head(df_umap_SnapATAC)
p_SnapATAC <- plot_umap(df_umap_SnapATAC,labels = labels,colormap = colormap,title='SnapATAC')
p_SnapATAC | _____no_output_____ | MIT | Extra/Cusanovich_2018_subset/test_blacklist/run_umap_cusanovich2018subset_no_blacklist_filtering.ipynb | tAndreani/scATAC-benchmarking |
SCRAT | df_umap_SCRAT <- run_umap(fm_SCRAT)
p_SCRAT <- plot_umap(df_umap_SCRAT,labels = labels,colormap = colormap,title='SCRAT')
p_SCRAT | _____no_output_____ | MIT | Extra/Cusanovich_2018_subset/test_blacklist/run_umap_cusanovich2018subset_no_blacklist_filtering.ipynb | tAndreani/scATAC-benchmarking |
Save feature matrices and UMAP coordinates | dataset = 'cusanovich2018subset_no_blacklist_filtering'
saveRDS(df_umap_SnapATAC,paste0(path_umap,'df_umap_SnapATAC.rds'))
saveRDS(df_umap_SCRAT,paste0(path_umap,'df_umap_SCRAT.rds'))
save.image(file = 'run_umap_cusanovich2018subset_no_blacklist_filtering.RData')
fig_width = 8
fig_height = 4
options(repr.plot.width=fi... | _____no_output_____ | MIT | Extra/Cusanovich_2018_subset/test_blacklist/run_umap_cusanovich2018subset_no_blacklist_filtering.ipynb | tAndreani/scATAC-benchmarking |
Learner> This contains fastai Learner extensions. | #export
from tsai.imports import *
from tsai.data.core import *
from tsai.data.validation import *
from tsai.models.all import *
from tsai.models.InceptionTimePlus import *
from fastai.learner import *
from fastai.vision.models.all import *
from fastai.data.transforms import *
#export
@patch
def show_batch(self:Learne... | _____no_output_____ | Apache-2.0 | nbs/052_learner.ipynb | clancy0614/tsai |
"Price Charts with Technical Indicators"> "Calculating Stock Price Indicators using FINTA python library, and visualizing using plotly python library."- toc: false- branch: master- badges: true- comments: true- author: Ijeoma Odoko- categories: [stocks, python, finta, pandas, plotly, ipywidgets] ![markus-spiske-69ppqH... | !pip install finta | Requirement already satisfied: finta in /usr/local/lib/python3.6/dist-packages (1.2)
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from finta) (1.18.5)
Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from finta) (1.1.2)
Requirement already satisfied: py... | Apache-2.0 | _notebooks/2020-10-23-Stock_Price_Indicators_using_the_python_FINTA_library.ipynb | IjeomaOdoko/my-blog |
Load required libraries | from pandas_datareader import data
import numpy as np
from datetime import datetime
from datetime import date, timedelta
import pandas as pd
# import ipwidgets library and functions
from __future__ import print_function
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
from IPython.disp... | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-23-Stock_Price_Indicators_using_the_python_FINTA_library.ipynb | IjeomaOdoko/my-blog |
Create widgets and dataframe for the stock data*Instructions for use. *1. Insert tuple of stock list. 2. Select stock from dropdown. 3. Select number of calendar days for dates from the last trading day. 4. Rerun all code after. | # Insert a tuple of unique tickers into the options variables.
#tickers = ('MMM', 'AOS', 'AAN', 'ABB', 'ABT', 'ABBV', 'ABM', 'ACN', 'AYI', 'GOLF', 'ADCT', 'ADT', 'AAP', 'ADSW', 'WMS', 'ACM', 'AEG', 'AER', 'AJRD', 'AMG', 'AFL', 'AGCO', 'A', 'AEM', 'ADC', 'AL', 'APD', 'AGI', 'ALK', 'ALB', 'ACI', 'AA', 'ALC', 'ARE', 'AQN'... | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-23-Stock_Price_Indicators_using_the_python_FINTA_library.ipynb | IjeomaOdoko/my-blog |
Download data for the stock | # create text to show stock ticker
v = widgets.Text(
value=stock_ticker.value,
description='Stockticker:',
disabled=True
)
# create function to load stock data from yahoo
def load_stock_data(stock_ticker, w):
start = date.today() - timedelta(w)
today = date.today()
stock_data = data.DataRead... | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-23-Stock_Price_Indicators_using_the_python_FINTA_library.ipynb | IjeomaOdoko/my-blog |
Calculate some Stock Price Indicators | # create example dataframe to try out the functions
ex_df = ohlcv.copy()
ex_df['RSI'] = TA.RSI(ex_df)
ex_df['Simple_Moving_Average_50'] = TA.SMA(ex_df, 50)
ex_df[['macd', 'macd_s']] = TA.MACD(ex_df)
ex_df | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-23-Stock_Price_Indicators_using_the_python_FINTA_library.ipynb | IjeomaOdoko/my-blog |
Create a function to create a dataframe that captures some stock technical indicators | # create function to create the Stock Indicator dataframe
def create_dataframe(df):
"""
This function creates a Dataframe for key indicators
"""
df['Daily_Returns'] = df['close'].pct_change() # create column for daily returns
df['Price_Up_or_Down'] = np.where(df['Daily_Returns'] < 0, -1... | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-23-Stock_Price_Indicators_using_the_python_FINTA_library.ipynb | IjeomaOdoko/my-blog |
VISUALIZATIONS USING PLOTLY Price Action Chart | # create OHLC charts with Plotly
import plotly.graph_objects as go
fig_ohlc = go.Figure(data=[go.Ohlc(x=stocks_df.index,
open=stocks_df['open'],
high=stocks_df['high'],
low=stocks_df['low'],
close=stocks_df['close'], showlegend=False)])
fig_ohl... | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-23-Stock_Price_Indicators_using_the_python_FINTA_library.ipynb | IjeomaOdoko/my-blog |
Momentum Indicators | #hide
import plotly.graph_objects as go
import plotly.offline as pyo
trace1 = go.Scatter(x=stocks_df.index, y=stocks_df['macd'], mode='lines', marker=dict(color="green"), showlegend=True, name='macd')
trace2 = go.Scatter(x=stocks_df.index, y=stocks_df['macd_signal'], mode='lines', marker=dict(color="blue"), showlegen... | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-23-Stock_Price_Indicators_using_the_python_FINTA_library.ipynb | IjeomaOdoko/my-blog |
Analyzing Hamlet | %load_ext autoreload
%autoreload 2
import src.data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from collections import OrderedDict
from IPython.display import display
pd.options.display.max_rows = 999
pd.options.display.max_columns = 999
pd.set_option("displa... | _____no_output_____ | MIT | notebooks/01.4_data_inventory.ipynb | martinlarsalbert/datadriven-energieffektivisering-av-fartyg |
Train mnist with Tensorflow**Requirements** - In order to benefit from this tutorial, you will need:- A basic understanding of Machine Learning- An Azure account with an active subscription - [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F)- An Azure ML workspace with computer cluster... | # import required libraries
from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential
from azure.ai.ml import MLClient
from azure.ai.ml.dsl import pipeline
from azure.ai.ml.entities import ResourceConfiguration | _____no_output_____ | MIT | sdk/jobs/pipelines/2a_train_mnist_with_tensorflow/train_mnist_with_tensorflow.ipynb | jplummer01/azureml-examples |
1.2 Configure credentialWe are using `DefaultAzureCredential` to get access to workspace. `DefaultAzureCredential` should be capable of handling most Azure SDK authentication scenarios. Reference for more available credentials if it does not work for you: [configure credential example](../../configuration.ipynb), [azu... | try:
credential = DefaultAzureCredential()
# Check if given credential can get token successfully.
credential.get_token("https://management.azure.com/.default")
except Exception as ex:
# Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work
credential = InteractiveBrowser... | _____no_output_____ | MIT | sdk/jobs/pipelines/2a_train_mnist_with_tensorflow/train_mnist_with_tensorflow.ipynb | jplummer01/azureml-examples |
1.3 Get a handle to the workspaceWe use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](../../configuration.ipynb) | # Get a handle to workspace
ml_client = MLClient.from_config(credential=credential)
# Retrieve an already attached Azure Machine Learning Compute.
cluster_name = "cpu-cluster"
print(ml_client.compute.get(cluster_name)) | _____no_output_____ | MIT | sdk/jobs/pipelines/2a_train_mnist_with_tensorflow/train_mnist_with_tensorflow.ipynb | jplummer01/azureml-examples |
2. Define command componentWe defined sample component using `command_component` decorator in [component.py](src/component.py). | with open("src/component.py") as fin:
print(fin.read())
%load_ext autoreload
%autoreload 2
from src.component import train_tf
help(train_tf) | _____no_output_____ | MIT | sdk/jobs/pipelines/2a_train_mnist_with_tensorflow/train_mnist_with_tensorflow.ipynb | jplummer01/azureml-examples |
3. Pipeline job 3.1 Build pipeline | @pipeline()
def train_mnist_with_tensorflow():
"""Train using TF component."""
tf_job = train_tf(epochs=1)
tf_job.compute = "cpu-cluster"
tf_job.resources = ResourceConfiguration(instance_count=2)
tf_job.distribution.worker_count = 2
tf_job.outputs.trained_model_output.mode = "upload"
# create... | _____no_output_____ | MIT | sdk/jobs/pipelines/2a_train_mnist_with_tensorflow/train_mnist_with_tensorflow.ipynb | jplummer01/azureml-examples |
3.2 Submit pipeline job | # submit job to workspace
pipeline_job = ml_client.jobs.create_or_update(
pipeline_job, experiment_name="pipeline_samples"
)
pipeline_job
# Wait until the job completes
ml_client.jobs.stream(pipeline_job.name) | _____no_output_____ | MIT | sdk/jobs/pipelines/2a_train_mnist_with_tensorflow/train_mnist_with_tensorflow.ipynb | jplummer01/azureml-examples |
import json
import datetime
input_dir = './'
output_dir = './'
filename = 'name'
data = {}
# Save
ts = datetime.datetime.utcnow().strftime('%y-%m-%dT%H-%M-%S') # so you don't overwrite, and save timestamp
with open(output_dir+f'{filename}_{ts}.json', "w") as fp:
json.dump(data, fp, indent=4)
# Load
with o... | _____no_output_____ | Apache-2.0 | load_write_json.ipynb | danielmlow/tutorials | |
Loading the model, uncertainities, and outcomes and generate experiments | # Here we only generate experiments for loading the necessary components.
#The actual results will be loaded in the next cell.
# Open Excel input data from the notebook directory before runnign the code in multi-processing.
# Close the folder where the results will be saved in multi-processing.
# This line must be at... | [MainProcess/INFO] results saved successfully to D:\moallemie\EM_analysis\Data\SDG_experiments_ranking_verification_GWP per Capita Indicator_sc500.tar.gz
| BSD-3-Clause | Notebook/.ipynb_checkpoints/Morris_ranking_verification_GWP per Capita Indicator-checkpoint.ipynb | enayatmoallemi/Moallemi_et_al_SDG_SSP_Assessment |
Calculating SA (Morris) metrics | # Morris mu_star index calculation as a function of number of scenarios and time
def make_morris_df(scores, problem, outcome_var, sc, t):
scores_filtered = {k:scores[k] for k in ['mu_star','mu_star_conf','mu','sigma']}
Si_df = pd.DataFrame(scores_filtered, index=problem['names'])
indices = Si_df[['mu... | _____no_output_____ | BSD-3-Clause | Notebook/.ipynb_checkpoints/Morris_ranking_verification_GWP per Capita Indicator-checkpoint.ipynb | enayatmoallemi/Moallemi_et_al_SDG_SSP_Assessment |
Where to draw the line between important and not important? | '''
Modifed from Waterprogramming blog by Antonia Hadgimichael: https://github.com/antonia-had/SA_verification
The idea is that we create 2 additiopnal Sets (current SA samples are Set 1).
We can create a Set 2, using only the T most important factors from our Set 1 sample,
and fixing all other factors to their defa... | [MainProcess/INFO] pool started
[MainProcess/INFO] performing 10500 scenarios * 1 policies * 1 model(s) = 10500 experiments
[MainProcess/INFO] 1050 cases completed
[MainProcess/INFO] 2100 cases completed
[MainProcess/INFO] 3150 cases completed
[MainProcess/INFO] 4200 cases completed
[MainProcess/INFO] 5250 cases comple... | BSD-3-Clause | Notebook/.ipynb_checkpoints/Morris_ranking_verification_GWP per Capita Indicator-checkpoint.ipynb | enayatmoallemi/Moallemi_et_al_SDG_SSP_Assessment |
Imports | import numpy as np
import pandas as pd
import os
import time
from itertools import permutations, combinations
from IPython.display import display | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
Prompts to choose which store you want | print("Welcome to Apriori 2.0!")
store_num = input("Please select your store \n 1. Amazon \n 2. Nike \n 3. Best Buy \n 4. K-Mart \n 5. Walmart\n")
print(store_num)
support_percent = input("Please enter the percentage of Support you want?\n")
print(support_percent)
confidence_percent = input("Please enter the percentage... | Welcome to Apriori 2.0!
Please select your store
1. Amazon
2. Nike
3. Best Buy
4. K-Mart
5. Walmart
1
1
Please enter the percentage of Support you want?
40
40
Please enter the percentage of Confidence you want?
40
40
| MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
These are my dictionaries to choose which store to get based in Key-Value Pairs | def number_to_store(store_number):
switcher = {
1: "data/amazon_transactions.csv",
2: "data/nike_transaction.csv",
3: "data/best_buy_transaction.csv",
4: "data/k_mart_transaction.csv",
5: "data/walmart_transaction.csv"
}
return switcher.get(store_number)
def number_... | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
We first have to read in the csv files and make sure that the inputs received from the user are valid | def a_priori_read(item_list, transaction, support_percentage, confidence_percentage):
# Create two different functions one that is solo for read in the file data and the other that is algorithmic with the data
if support_percentage > 100 or confidence_percentage > 100 or support_percentage < 0 or confidence_per... | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
The first go around of the Apriori Algorithm we find the items that are most frequent when K=1 This is so that we can find the most frequent items given the transactions | def ap_1(items_names, trans, support_percentage, confidence_percentage, k_value):
counter = np.zeros(len(items_names), dtype=int)
for i in trans:
i = list((map(str.strip, i.split(','))))
s1 = set(i)
nums = 0
for x in items_names:
s2 = set()
s2.add(x)
... | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
Then we use this function below to find item sets that are most frequent when K > 1 | def ap_2(item_comb, k_value, trans, support_percentage):
boo = True
comb = combinations(item_comb, k_value)
comb = list(comb)
counter = np.zeros(len(comb), dtype=int)
if k_value > 1:
for i in trans:
i = list((map(str.strip, i.split(','))))
s1 = set(i)
nums... | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
Calls of functions and variable saving | frames = []
items_names, trans, support_percent, confidence_percent, k_value = a_priori_read(
str(number_to_item_list_of_store(int(store_num))), str(number_to_store(int(store_num))),
int(support_percent), int(confidence_percent))
items, supp, k_value, df = ap_1(items_names, trans, support_percent, confidence_p... | transaction_id \
0 1
1 2
2 3
3 4
4 5
transaction
0 A Beginner’s Guide, Java: The Complete Reference... | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
Increasing K by 1 until we can longer support the support value | while boo:
df_1, boo = ap_2(items, k_value, trans, support_percent)
frames.append(df_1)
k_value += 1 | When K = 2
item_name support k_val
0 (A Beginner’s Guide, Java: The Complete Reference) 45 2
1 (A Beginner’s Guide, Java For Dummies) 45 2
4 (Java: The Complete Reference, Java For Dummies) 50 ... | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
Combine the dataframes we have from when we increase K | print("results of item-sets that meet support are below")
display(pd.concat(frames))
df_supp = pd.concat(frames)
# df_supp.head() | results of item-sets that meet support are below
| MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
Reset the index just to organize it and the results after we find the most frequent sets in the list of transactions | df_supp = df_supp.reset_index().drop('index',axis=1)
df_supp | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
This is the FUNCTION that genrerates the Associations (Permutations) and calculating the Confidence of the item sets | def confidence(val):
#Since we already have our support for our items what we need to worry about is the confidence levels
#item_set before the arrow
df_before = df_supp.loc[df_supp['k_val'] == val]
stuff_name_before = np.array(df_before["item_name"])
support_arr_before = np.array(df_befo... | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
Finding the max k value in the given set | try:
max(df_supp["k_val"])
except:
print("No max was found...") | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
This is where I iteratively call the confidence() function | df_frames = []
try:
if len(df_supp["k_val"]) != 0 :
for lp in range(1,max(df_supp["k_val"])+1):
#print(lp)
df_0 = confidence(lp)
df_0 = df_0[df_0.support_sing != 0]
df_frames.append(df_0)
df_associations = pd.concat(df_frames)
display(df_assoc... | _____no_output_____ | MIT | .ipynb_checkpoints/Apriori_Algo-checkpoint.ipynb | MichaelWoo-git/Apriori_Algorithm_from_Scratch |
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