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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Design 2A | sid='../Data/Design2a/design2a_11k_test5/'
rp_sessionDes2 = ra.Session(stype='radical.pilot',src=sid)
unitsDes2 = rp_sessionDes2.filter(etype='unit', inplace=False)
execUnits = unitsDes2.filter(uid=['unit.000002','unit.000003','unit.000004','unit.000001'],inplace=False)
exec_units_setup_des2 = execUnits.duration(event=... | _____no_output_____ | MIT | Geolocation/Notebooks/Des1Des2OverheadComp.ipynb | radical-experiments/iceberg_escience |
Running, Debugging, Testing & Packaging | !code ./1-helloconnectedworld | _____no_output_____ | MIT | 1_RunExtension.ipynb | kevcunnane/msbuild_ads_demo |
NetworksIn this last exercise, you will start to put into practice some of the concepts learned in class. Start by importing the libraries that you will need. | import networkx as nx
import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | MIT | ix-1-lab-intro/4-networks.ipynb | emlg/Internet-Analytics |
We give you the network of the 34 members of [karate clubs](https://en.wikipedia.org/wiki/Zachary's_karate_club) on the campus. Each node is a member of one of two clubs ("Mr. Hi" or "Officer") and there is an edge between two nodes if the two are friends outside of the club. | karate_club = nx.karate_club_graph()
| _____no_output_____ | MIT | ix-1-lab-intro/4-networks.ipynb | emlg/Internet-Analytics |
Visualization1. Visualize the network by - painting every 'Mr. Hi' member in **blue** and every 'Officer' member in **red**, and - drawing the label corresponding to the member index (between 1 and 34) on each node.2. What can you tell about the nodes and their connections? Do you see some nodes with more/less l... | # Your code goes here
color_map = []
for i in range(0, karate_club.order()):
if karate_club.node[i]['club'] == "Mr. Hi":
color_map.append('blue')
else:
color_map.append('red')
nx.draw_networkx(karate_club, node_color = color_map, with_labels = True)
plt.axis('off'); | _____no_output_____ | MIT | ix-1-lab-intro/4-networks.ipynb | emlg/Internet-Analytics |
Degree Distribution1. Plot the degree distribution of the network.2. Does it confirm your answer to the previous question? | # Your code goes here
degre = list()
for v in karate_club:
degre.append(karate_club.degree(v))
plt.plot(degre, '*')
plt.show() | _____no_output_____ | MIT | ix-1-lab-intro/4-networks.ipynb | emlg/Internet-Analytics |
Convolutional AutoencoderSticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data. | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r') | _____no_output_____ | MIT | autoencoder/Convolutional_Autoencoder_Solution.ipynb | vinidixit/deep-learning-repo |
Network ArchitectureThe encoder part of the network will be a typical convolutional pyramid. Each convolutional layer will be followed by a max-pooling layer to reduce the dimensions of the layers. The decoder though might be something new to you. The decoder needs to convert from a narrow representation to a wide rec... | inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')
targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')
### Encoder
conv1 = tf.layers.conv2d(inputs_, 16, (3,3), padding='same', activation=tf.nn.relu)
# Now 28x28x16
maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding=... | _____no_output_____ | MIT | autoencoder/Convolutional_Autoencoder_Solution.ipynb | vinidixit/deep-learning-repo |
TrainingAs before, here wi'll train the network. Instead of flattening the images though, we can pass them in as 28x28x1 arrays. | sess = tf.Session()
epochs = 1
batch_size = 200
sess.run(tf.global_variables_initializer())
for e in range(epochs):
for ii in range(mnist.train.num_examples//batch_size):
batch = mnist.train.next_batch(batch_size)
imgs = batch[0].reshape((-1, 28, 28, 1))
batch_cost, _ = sess.run([cost, opt],... | _____no_output_____ | MIT | autoencoder/Convolutional_Autoencoder_Solution.ipynb | vinidixit/deep-learning-repo |
DenoisingAs I've mentioned before, autoencoders like the ones you've built so far aren't too useful in practive. However, they can be used to denoise images quite successfully just by training the network on noisy images. We can create the noisy images ourselves by adding Gaussian noise to the training images, then cl... | inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')
targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')
### Encoder
conv1 = tf.layers.conv2d(inputs_, 32, (3,3), padding='same', activation=tf.nn.relu)
# Now 28x28x32
maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding=... | _____no_output_____ | MIT | autoencoder/Convolutional_Autoencoder_Solution.ipynb | vinidixit/deep-learning-repo |
Checking out the performanceHere I'm adding noise to the test images and passing them through the autoencoder. It does a suprising great job of removing the noise, even though it's sometimes difficult to tell what the original number is. | fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))
in_imgs = mnist.test.images[:10]
noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)
noisy_imgs = np.clip(noisy_imgs, 0., 1.)
reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))... | _____no_output_____ | MIT | autoencoder/Convolutional_Autoencoder_Solution.ipynb | vinidixit/deep-learning-repo |
1.1. Create_Data_Augmentation_With_Augmentorhttps://github.com/mdbloice/Augmentor | # install the package
# !pip install Augmentor
import Augmentor
label = '200_frente'
path_img = 'data_augmentation/source/' + label + '/'
sample_per_configurator = 50
# random distortion + rotate
p1 = Augmentor.Pipeline(path_img)
p1.rotate(probability=1.0, max_left_rotation=20, max_right_rotation=20)
p1.random_distorti... |
Executing Pipeline: 0%| | 0/25 [00:00<?, ? Samples/s] | MIT | 1.1. Create_Data_Augmentation_With_Augmentor.ipynb | christianquicano/tflite_classification_banknotes |
Practice geospatial aggregations in geopandas before writing them to .py files | %load_ext autoreload
%autoreload 2
import sys
sys.path.append('../utils')
import wd_management
wd_management.set_wd_root()
import geopandas as gp
import pandas as pd
import requests
res = requests.get('https://services5.arcgis.com/GfwWNkhOj9bNBqoJ/arcgis/rest/services/NYC_Public_Use_Microdata_Areas_PUMAs_2010/FeatureS... | _____no_output_____ | MIT | notebooks/Area_within_Historic_District_Exploratory.ipynb | NYCPlanning/db-equitable-development-tool |
Ok looks good. Load in historic districts. [This stackoverflow post](https://gis.stackexchange.com/questions/327197/typeerror-input-geometry-column-must-contain-valid-geometry-objects) was helpful | from shapely import wkt
hd= gp.read_file('.library/lpc_historic_district_areas.csv')
hd['the_geom'] = hd['the_geom'].apply(wkt.loads)
hd.set_geometry(col='the_geom', inplace=True, crs='EPSG:4326')
hd= hd.explode(column='the_geom')
hd.set_geometry('the_geom',inplace=True)
hd = hd.to_crs('EPSG:2263')
hd = hd.reset_index(... | /var/folders/tg/01b257jj0dzg5fzzc3p3gj0w0000gq/T/ipykernel_26102/3499734924.py:1: FutureWarning: Currently, index_parts defaults to True, but in the future, it will default to False to be consistent with Pandas. Use `index_parts=True` to keep the current behavior and True/False to silence the warning.
hd= hd.explode(... | MIT | notebooks/Area_within_Historic_District_Exploratory.ipynb | NYCPlanning/db-equitable-development-tool |
Ok great next do some geospatial analysis. Start only with PUMA 3807 as it has a lot of historic area | def fraction_area_historic(PUMA, hd):
try:
gdf = gp.GeoDataFrame(geometry = [PUMA.geometry], crs = 'EPSG:4326')
gdf = gdf.to_crs('EPSG:2263')
overlay = gp.overlay(hd, gdf, 'intersection')
if overlay.empty:
return 0, 0
else:
fraction = overlay.area.sum... | _____no_output_____ | MIT | notebooks/Area_within_Historic_District_Exploratory.ipynb | NYCPlanning/db-equitable-development-tool |
Superimpose PUMA 3801's historic districts on it to see if 38% looks right | def visualize_overlay(PUMA):
test_PUMA = NYC_PUMAs.loc[[PUMA]].to_crs('EPSG:2263')
base = test_PUMA.plot(color='green', edgecolor='black')
overlay = gp.overlay(hd, test_PUMA, 'intersection')
overlay.plot(ax=base, color='red');
visualize_overlay('3810') | _____no_output_____ | MIT | notebooks/Area_within_Historic_District_Exploratory.ipynb | NYCPlanning/db-equitable-development-tool |
Ok great that looks like about a third to me From eyeballing map, more than 20% of PUMA 3806 on UWS looks to be historic | visualize_overlay('3806') | _____no_output_____ | MIT | notebooks/Area_within_Historic_District_Exploratory.ipynb | NYCPlanning/db-equitable-development-tool |
Ah ok the PUMA geography from includes central park. Worth flagging Question from Renae:Renae points out that description of historic districts says "including items that may have been denied designation or overturned."Look at dataset to see if columns point to this clearly | hd.head(5)
hd.groupby('status_of_').size()
hd.groupby('current_').size()
hd.groupby('last_actio').size() | _____no_output_____ | MIT | notebooks/Area_within_Historic_District_Exploratory.ipynb | NYCPlanning/db-equitable-development-tool |
Install Transformers Library | !pip install transformers
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import transformers
from transformers import AutoModel, BertTokenizerFast
# specify GPU
device = torch.device("cuda"... | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Load Dataset | df = pd.read_csv("spamdata_v2.csv")
df.head()
df.shape
# check class distribution
df['label'].value_counts(normalize = True) | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Split train dataset into train, validation and test sets | train_text, temp_text, train_labels, temp_labels = train_test_split(df['text'], df['label'],
random_state=2018,
test_size=0.3,
... | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Import BERT Model and BERT Tokenizer | # import BERT-base pretrained model
bert = AutoModel.from_pretrained('bert-base-uncased')
# Load the BERT tokenizer
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
# sample data
text = ["this is a bert model tutorial", "we will fine-tune a bert model"]
# encode text
sent_id = tokenizer.batch_encode... | {'input_ids': [[101, 2023, 2003, 1037, 14324, 2944, 14924, 4818, 102, 0], [101, 2057, 2097, 2986, 1011, 8694, 1037, 14324, 2944, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
| Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Tokenization | # get length of all the messages in the train set
seq_len = [len(i.split()) for i in train_text]
pd.Series(seq_len).hist(bins = 30)
max_seq_len = 25
# tokenize and encode sequences in the training set
tokens_train = tokenizer.batch_encode_plus(
train_text.tolist(),
max_length = max_seq_len,
pad_to_max_leng... | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Convert Integer Sequences to Tensors | # for train set
train_seq = torch.tensor(tokens_train['input_ids'])
train_mask = torch.tensor(tokens_train['attention_mask'])
train_y = torch.tensor(train_labels.tolist())
# for validation set
val_seq = torch.tensor(tokens_val['input_ids'])
val_mask = torch.tensor(tokens_val['attention_mask'])
val_y = torch.tensor(val... | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Create DataLoaders | from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
#define a batch size
batch_size = 32
# wrap tensors
train_data = TensorDataset(train_seq, train_mask, train_y)
# sampler for sampling the data during training
train_sampler = RandomSampler(train_data)
# dataLoader for train set... | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Freeze BERT Parameters | # freeze all the parameters
for param in bert.parameters():
param.requires_grad = False | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Define Model Architecture | class BERT_Arch(nn.Module):
def __init__(self, bert):
super(BERT_Arch, self).__init__()
self.bert = bert
# dropout layer
self.dropout = nn.Dropout(0.1)
# relu activation function
self.relu = nn.ReLU()
# dense layer 1
self.fc1 = nn.Linear(768,... | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Find Class Weights | from sklearn.utils.class_weight import compute_class_weight
#compute the class weights
class_wts = compute_class_weight('balanced', np.unique(train_labels), train_labels)
print(class_wts)
# convert class weights to tensor
weights= torch.tensor(class_wts,dtype=torch.float)
weights = weights.to(device)
# loss function... | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Fine-Tune BERT | # function to train the model
def train():
model.train()
total_loss, total_accuracy = 0, 0
# empty list to save model predictions
total_preds=[]
# iterate over batches
for step,batch in enumerate(train_dataloader):
# progress update after every 50 batches.
if step % 50 == 0 and not st... | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Start Model Training | # set initial loss to infinite
best_valid_loss = float('inf')
# empty lists to store training and validation loss of each epoch
train_losses=[]
valid_losses=[]
#for each epoch
for epoch in range(epochs):
print('\n Epoch {:} / {:}'.format(epoch + 1, epochs))
#train model
train_loss, _ = train()
... |
Epoch 1 / 10
Batch 50 of 122.
Batch 100 of 122.
Evaluating...
Training Loss: 0.526
Validation Loss: 0.656
Epoch 2 / 10
Batch 50 of 122.
Batch 100 of 122.
Evaluating...
Training Loss: 0.345
Validation Loss: 0.231
Epoch 3 / 10
Batch 50 of 122.
Batch 100 of 122.... | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Load Saved Model | #load weights of best model
path = 'saved_weights.pt'
model.load_state_dict(torch.load(path)) | _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
Get Predictions for Test Data | # get predictions for test data
with torch.no_grad():
preds = model(test_seq.to(device), test_mask.to(device))
preds = preds.detach().cpu().numpy()
# model's performance
preds = np.argmax(preds, axis = 1)
print(classification_report(test_y, preds))
# confusion matrix
pd.crosstab(test_y, preds)
| _____no_output_____ | Apache-2.0 | Fine_Tuning_BERT_for_Spam_Classification.ipynb | lamiathu/Fine-Tuning-BERT |
WeatherPy---- Note* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps. | # Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import time
import random
import json
import scipy.stats as st
from scipy.stats import linregress
# Import API key
from api_keys import weather_api_key
from api_keys import g_key
# Incorporated citipy to de... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Generate Cities List | # List for holding lat_lngs and cities
lat_lngs = []
cities = []
# Create a set of random lat and lng combinations
lats = np.random.uniform(lat_range[0], lat_range[1], size=1500)
lngs = np.random.uniform(lng_range[0], lng_range[1], size=1500)
lat_lngs = zip(lats, lngs)
# Identify nearest city for each lat, lng combin... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Perform API Calls* Perform a weather check on each city using a series of successive API calls.* Include a print log of each city as it'sbeing processed (with the city number and city name). | print("Beginning Data Retrieval")
print("-------------------------------")
base_url = "http://api.openweathermap.org/data/2.5/weather?"
units = "imperial"
url = f"{base_url}appid={weather_api_key}&units={units}&q="
for index, city in enumerate(cities, start = 1):
try:
response = requests.get(url + city).j... | Beginning Data Retrieval
-------------------------------
Processing Record 1 of Set 1 : albany
Processing Record 2 of Set 1 : terney
City not found. Skipping...
Processing Record 3 of Set 1 : pedasi
Processing Record 4 of Set 1 : wakkanai
Processing Record 5 of Set 1 : talnakh
City not found. Skipping...
Processing Rec... | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Convert Raw Data to DataFrame* Export the city data into a .csv.* Display the DataFrame | weather_df = pd.DataFrame({
"City" : city_names_ls,
"Cloudiness" : cloudiness_ls,
"Country" : country_ls,
"Date" : date_ls,
"Humidity" : humidity_ls,
"Lat" : lat_ls,
"Lng" : lng_ls,
"Max Temp... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Inspect the data and remove the cities where the humidity > 100%.----Skip this step if there are no cities that have humidity > 100%. | # Get the indices of cities that have humidity over 100%.
# Make a new DataFrame equal to the city data to drop all humidity outliers by index.
# Passing "inplace=False" will make a copy of the city_data DataFrame, which we call "clean_city_data".
# Extract relevant fields from the data frame
# Export the City_Dat... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Plotting the Data* Use proper labeling of the plots using plot titles (including date of analysis) and axes labels.* Save the plotted figures as .pngs. Latitude vs. Temperature Plot | plt.scatter(weather_df["Lat"], weather_df["Max Temp"], facecolor = "darkblue", edgecolor = "darkgrey")
plt.title("City Latitude vs. Max Temperature (07/13/20)")
plt.ylabel("Max Temperature (F)")
plt.xlabel("Latitude")
plt.grid(True)
plt.savefig("../Images/City Latitude vs Max Temperature.png")
plt.show()
print("Th... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Latitude vs. Humidity Plot | plt.scatter(weather_df["Lat"], weather_df["Humidity"], facecolor = "darkblue", edgecolor = "darkgrey")
plt.title("City Latitude vs. Humidity (07/13/20)")
plt.ylabel("Humidity (%)")
plt.xlabel("Latitude")
plt.grid(True)
plt.savefig("../Images/City Latitude vs Humidity.png")
plt.show()
print("This plot shows that hu... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Latitude vs. Cloudiness Plot | plt.scatter(weather_df["Lat"], weather_df["Cloudiness"], facecolor = "darkblue", edgecolor = "darkgrey")
plt.title("City Latitude vs. Cloudiness (07/13/20)")
plt.ylabel("Cloudiness (%)")
plt.xlabel("Latitude")
plt.grid(True)
plt.savefig("../Images/City Latitude vs Cloudiness.png")
plt.show()
print("This plot shows... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Latitude vs. Wind Speed Plot | plt.scatter(weather_df["Lat"], weather_df["Wind Speed"], facecolor = "darkblue", edgecolor = "darkgrey")
plt.title("City Latitude vs. Windspeed (07/13/20)")
plt.ylabel("Windspeed (mph)")
plt.xlabel("Latitude")
plt.grid(True)
plt.savefig("../Images/City Latitude vs Windspeed.png")
plt.show()
print("This plot shows ... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Linear Regression | # OPTIONAL: Create a function to create Linear Regression plots
nor_hemi = weather_df.loc[weather_df["Lat"] >= 0]
sou_hemi = weather_df.loc[weather_df["Lat"] < 0]
def linear_agression(x,y):
print(f"The r-squared is : {round(st.pearsonr(x, y)[0],2)}")
(slope, intercept, rvalue, pvalue, stderr) = linregress(x, y)... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Northern Hemisphere - Max Temp vs. Latitude Linear Regression | equation = linear_agression(nor_hemi["Lat"], nor_hemi["Max Temp"])
annotate(equation, 10, 40)
plt.ylabel("Max Temp (F)")
plt.xlabel("Latitude")
plt.savefig("../Images/NorHemi Max Temp vs. Latitude Linear Regression.png")
print("This linear regression shows that cities gets hotter as get closer to equator in northe... | The r-squared is : -0.65
This linear regression shows that cities gets hotter as get closer to equator in northern hemisphere
| ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Southern Hemisphere - Max Temp vs. Latitude Linear Regression | equation = linear_agression(sou_hemi["Lat"], sou_hemi["Max Temp"])
annotate(equation, -50, 80)
plt.ylabel("Max Temperature (F)")
plt.xlabel("Latitude")
plt.savefig("../Images/SouHemi Max Temp vs. Latitude Linear Regression.png")
print("This linear regression shows that cities gets colder as get away to equator in so... | The r-squared is : 0.74
| ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Northern Hemisphere - Humidity (%) vs. Latitude Linear Regression | equation = linear_agression(nor_hemi["Lat"], nor_hemi["Humidity"])
annotate(equation, 1, 5)
plt.ylabel("Humidity")
plt.xlabel("Latitude")
plt.savefig("../Images/NorHemi Humidity vs. Latitude Linear Regression.png")
print("This linear regression shows that cities' humidity doesn't change much as get closer to equa... | The r-squared is : -0.08
| ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Southern Hemisphere - Humidity (%) vs. Latitude Linear Regression | equation = linear_agression(sou_hemi["Lat"], sou_hemi["Humidity"])
annotate(equation, -50, 20)
plt.ylabel("Humidity")
plt.xlabel("Latitude")
plt.savefig("../Images/SouHemi Humidity vs. Latitude Linear Regression.png")
print("This linear regression shows that cities' humidity doesn't change much as get closer to equa... | The r-squared is : 0.09
| ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Northern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression | equation = linear_agression(nor_hemi["Lat"], nor_hemi["Cloudiness"])
annotate(equation, 0, 0)
plt.ylabel("Cloudiness")
plt.xlabel("Latitude")
plt.savefig("../Images/NorHemi Cloudiness vs. Latitude Linear Regression.png")
print("This linear regression shows that cities' cloudiness doesn't change much as get closer... | The r-squared is : -0.01
| ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Southern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression | equation = linear_agression(sou_hemi["Lat"], sou_hemi["Cloudiness"])
annotate(equation, -50, 50)
plt.ylabel("Cloudiness")
plt.xlabel("Latitude")
plt.savefig("../Images/SouHemi Cloudiness vs. Latitude Linear Regression.png")
print("This linear regression shows that cities' cloudiness doesn't change much as get closer... | The r-squared is : 0.1
| ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Northern Hemisphere - Wind Speed (mph) vs. Latitude Linear Regression | equation = linear_agression(nor_hemi["Lat"], nor_hemi["Wind Speed"])
annotate(equation, 40, 25)
plt.ylabel("Wind Speed")
plt.xlabel("Latitude")
plt.savefig("../Images/NorHemi Wind Speed vs. Latitude Linear Regression.png")
print("This linear regression shows that cities' windiness doesn't change much as get close... | The r-squared is : -0.0
| ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
Southern Hemisphere - Wind Speed (mph) vs. Latitude Linear Regression | equation = linear_agression(sou_hemi["Lat"], sou_hemi["Wind Speed"])
annotate(equation, -50, 25)
plt.ylabel("Wind Speed")
plt.xlabel("Latitude")
plt.savefig("../Images/SouHemi Wind Speed vs. Latitude Linear Regression.png")
print("This linear regression shows that cities' windiness doesn't change much as get closer ... | _____no_output_____ | ADSL | 06-Python-APIs_HW/Instructions/starter_code/WeatherPy.ipynb | jmbruner37/6_python_api |
"""
Array2D
"""
class Array2D:
def __init__(self,rows, cols, value):
self.__cols = cols
self.__rows = rows
self.__array=[[value for x in range(self.__cols)] for y in range(self.__rows)]
def to_string(self):
[print("---",end="") for x in range(self.__cols)]
print("")
... | 7 7 [6, 2]
['1,1,1,1,1,1,1\n', '1,1,1,1,1,1,1\n', '1,1,1,1,1,1,1\n', '1,1,1,1,1,1,1\n', '1,0,0,0,0,0,S\n', '1,1,0,1,1,1,1\n', '1,1,E,1,1,1,1']
---------------------
['1', '1', '1', '1', '1', '1', '1']
['1', '1', '1', '1', '1', '1', '1']
['1', '1', '1', '1', '1', '1', '1']
['1', '1', '1', '1', '1', '1', '1']
['1', '0', ... | MIT | 4_diciembre1358.ipynb | jorge23amury/daa_2021_1 | |
Run hacked AlphaFold2 on the designed bound states Imports | %load_ext lab_black
# Python standard library
from glob import glob
import os
import socket
import sys
# 3rd party library imports
import dask
import matplotlib.pyplot as plt
import pandas as pd
import pyrosetta
import numpy as np
import scipy
import seaborn as sns
from tqdm.auto import tqdm # jupyter compatible prog... | running in directory: /mnt/home/pleung/projects/crispy_shifty/projects/crispy_shifties
running on node: dig154
| MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Set working directory to the root of the crispy_shifty repoTODO set to projects dir | os.chdir("/home/pleung/projects/crispy_shifty")
# os.chdir("/projects/crispy_shifty") | _____no_output_____ | MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Run AF2 on the designed bound statesTODO | from crispy_shifty.utils.io import gen_array_tasks
simulation_name = "03_fold_bound_states"
design_list_file = os.path.join(
os.getcwd(),
"projects/crispy_shifties/02_mpnn_bound_states/test_mpnn_states.pair", # TODO
)
output_path = os.path.join(os.getcwd(), f"projects/crispy_shifties/{simulation_name}")
opti... | _____no_output_____ | MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Collect scorefiles of designed bound states and concatenateTODO change to projects dir | sys.path.insert(0, "~/projects/crispy_shifty") # TODO
from crispy_shifty.utils.io import collect_score_file
simulation_name = "03_fold_bound_states"
output_path = os.path.join(os.getcwd(), f"projects/crispy_shifties/{simulation_name}")
if not os.path.exists(os.path.join(output_path, "scores.json")):
collect_scor... | /projects/crispy_shifty/envs/crispy/lib/python3.8/site-packages/dask_jobqueue/core.py:20: FutureWarning: tmpfile is deprecated and will be removed in a future release. Please use dask.utils.tmpfile instead.
from distributed.utils import tmpfile
| MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Load resulting concatenated scorefileTODO change to projects dir | sys.path.insert(0, "~/projects/crispy_shifty") # TODO
from crispy_shifty.utils.io import parse_scorefile_linear
output_path = os.path.join(os.getcwd(), f"projects/crispy_shifties/{simulation_name}")
scores_df = parse_scorefile_linear(os.path.join(output_path, "scores.json"))
scores_df = scores_df.convert_dtypes() | _____no_output_____ | MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Setup for plotting | sns.set(
context="talk",
font_scale=1, # make the font larger; default is pretty small
style="ticks", # make the background white with black lines
palette="colorblind", # a color palette that is colorblind friendly!
) | _____no_output_____ | MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Data explorationGonna remove the Rosetta sfxn scoreterms for now | from crispy_shifty.protocols.design import beta_nov16_terms
scores_df = scores_df[
[term for term in scores_df.columns if term not in beta_nov16_terms]
]
print(len(scores_df))
j = 0
for i, r in scores_df.iterrows():
if (r["designed_by"]) == "rosetta":
j += 1
print(j) | 10
| MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Save a list of outputs | # simulation_name = "03_fold_bound_states"
# output_path = os.path.join(os.getcwd(), f"projects/crispy_shifties/{simulation_name}")
# with open(os.path.join(output_path, "folded_states.list"), "w") as f:
# for path in tqdm(scores_df.index):
# print(path, file=f) | _____no_output_____ | MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Prototyping blocks test `fold_bound_state` | %%time
from operator import gt, lt
import pyrosetta
filter_dict = {
"mean_plddt": (gt, 85.0),
"rmsd_to_reference": (lt, 2.2),
"mean_pae_interaction": (lt, 10.0),
}
rank_on = "mean_plddt"
prefix = "mpnn_seq"
pyrosetta.init()
sys.path.insert(0, "~/projects/crispy_shifty/") # TODO projects
from crispy_sh... | _____no_output_____ | MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
test `generate_decoys_from_pose` | from operator import gt, lt
from crispy_shifty.protocols.folding import generate_decoys_from_pose
filter_dict = {
"mean_plddt": (gt, 85.0),
"rmsd_to_reference": (lt, 2.2),
"mean_pae_interaction": (lt, 10.0),
}
rank_on = "mean_plddt"
prefix = "mpnn_seq"
tpose = tppose.pose.clone()
genr = generate_decoys_... | _____no_output_____ | MIT | projects/crispy_shifties/03_fold_bound_states.ipynb | proleu/crispy_shifty |
Segmentation of Road from Satellite imagery Importing Libraries | import warnings
warnings.filterwarnings('ignore')
import os
import cv2
#from google.colab.patches import cv2_imshow
import numpy as np
import tensorflow as tf
import pandas as pd
from keras.models import Model, load_model
from skimage.morphology import label
import pickle
from keras import backend as K
from matplotli... | Drive already mounted at /content/gdrive/; to attempt to forcibly remount, call drive.mount("/content/gdrive/", force_remount=True).
/content/gdrive/My Drive/MapSegClean
| MIT | Train_and_Test_Notebooks/ResNet101_RoadTest.ipynb | parshwa1999/Map-Segmentation |
Defining Custom Loss functions and accuracy Metric. | #Source: https://towardsdatascience.com/metrics-to-evaluate-your-semantic-segmentation-model-6bcb99639aa2
from keras import backend as K
def iou_coef(y_true, y_pred, smooth=1):
intersection = K.sum(K.abs(y_true * y_pred), axis=[1,2,3])
union = K.sum(y_true,[1,2,3])+K.sum(y_pred,[1,2,3])-intersection
iou = K.mean(... | _____no_output_____ | MIT | Train_and_Test_Notebooks/ResNet101_RoadTest.ipynb | parshwa1999/Map-Segmentation |
Defining Our Model | pip install -U segmentation-models
from keras.models import Model, load_model
import tensorflow as tf
from keras.layers import Input
from keras.layers.core import Dropout, Lambda
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.pooling import MaxPooling2D
from keras.layers.merge import c... | WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_grap... | MIT | Train_and_Test_Notebooks/ResNet101_RoadTest.ipynb | parshwa1999/Map-Segmentation |
HYPER_PARAMETERS | LEARNING_RATE = 0.0001 | _____no_output_____ | MIT | Train_and_Test_Notebooks/ResNet101_RoadTest.ipynb | parshwa1999/Map-Segmentation |
Initializing Callbacks | #from tensorboardcolab import TensorBoardColab, TensorBoardColabCallback
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from datetime import datetime
model_path = "./Models/Resnet_road_weights.h5"
checkpointer = ModelCheckpoint(model_path,
monitor="val_loss",
... | _____no_output_____ | MIT | Train_and_Test_Notebooks/ResNet101_RoadTest.ipynb | parshwa1999/Map-Segmentation |
Compiling the model | opt = keras.optimizers.adam(LEARNING_RATE)
model.compile(
optimizer=opt,
loss=soft_dice_loss,
metrics=[iou_coef]) | WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
| MIT | Train_and_Test_Notebooks/ResNet101_RoadTest.ipynb | parshwa1999/Map-Segmentation |
Testing our Model On Test Images | model.load_weights("Models/Resnet_road_weights.h5")
import cv2
import glob
import numpy as np
import h5py
#test_images = np.array([cv2.imread(file) for file in glob.glob("/home/bisag/Desktop/Road-Segmentation/I/")])
#test_masks = np.array([cv2.imread(file) for file in glob.glob("/home/bisag/Desktop/Road-Segmentation/M... | _____no_output_____ | MIT | Train_and_Test_Notebooks/ResNet101_RoadTest.ipynb | parshwa1999/Map-Segmentation |
Just Test | """Test"""
import cv2
import glob
import numpy as np
import h5py
#test_images = np.array([cv2.imread(file) for file in glob.glob("/home/bisag/Desktop/Road-Segmentation/I/")])
#test_masks = np.array([cv2.imread(file) for file in glob.glob("/home/bisag/Desktop/Road-Segmentation/M/")])
test_images = []
files = glob.glob... | _____no_output_____ | MIT | Train_and_Test_Notebooks/ResNet101_RoadTest.ipynb | parshwa1999/Map-Segmentation |
Seaborn (Working on the diabetes dataset) | import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('diabetes.csv')
sns.lmplot(x='Age', y='BloodPressure', data=df)
plt.show()
sns.lmplot(x='Age', y='Glucose', data=df,
fit_reg=False, # No regression line
hue='Outcome' # color by evaluation stage
)... | _____no_output_____ | MIT | Session_2_ and_3_Seaborn.ipynb | reeha-parkar/applied-statistics |
Homework 4Today we'll start by reproducing the DQN and then try improving it with the tricks we learned on the lecture:* Target networks* Double q-learning* Prioritized experience replay* Dueling DQN* Bootstrap DQN | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# If you are running on a server, launch xvfb to record game videos
# Please make sure you have xvfb installed
import os
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY")) == 0:
!bash ../xvfb start
os.environ['D... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Processing game image (2 pts)Raw Atari images are large, 210x160x3 by default. However, we don't need that level of detail in order to learn them.We can thus save a lot of time by preprocessing game image, including* Resizing to a smaller shape* Converting to grayscale* Cropping irrelevant image parts | from gym.core import ObservationWrapper
from gym.spaces import Box
from scipy.misc import imresize
class PreprocessAtari(ObservationWrapper):
def __init__(self, env):
"""A gym wrapper that crops, scales image into the desired shapes and optionally grayscales it."""
ObservationWrapper.__init__(sel... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Building a DQN (2 pts)Here we define a simple agent that maps game images into Qvalues using simple convolutional neural network. | # setup theano/lasagne. Prefer GPU. Fallback to CPU (will print warning)
%env THEANO_FLAGS = floatX = float32
import theano
import lasagne
from lasagne.layers import *
from theano import tensor as T
# observation
observation_layer = InputLayer(
(None,)+observation_shape) # game image, [batch,64,64]
# 4-tick windo... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Define agentHere you will need to declare how your agent works* `observation_layers` and `action_layers` are the input and output of agent in MDP.* `policy_estimators` must contain whatever you need for training * In our case, that's `qvalues_layer`, but you'll need to add more when implementing target network.* agent... | from agentnet.agent import Agent
# agent
agent = Agent(observation_layers=<YOUR CODE>,
policy_estimators=<YOUR CODE>,
action_layers=<YOUR CODE>,
agent_states={new_wnd: prev_wnd},) | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Create and manage a pool of Atari sessions to play with* To make training more stable, we shall have an entire batch of game sessions each happening independent of others* Why several parallel agents help training: http://arxiv.org/pdf/1602.01783v1.pdf* Alternative approach: store more sessions: https://www.cs.toronto... | from agentnet.experiments.openai_gym.pool import EnvPool
pool = EnvPool(agent, make_env, n_games=16) # 16 parallel game sessions
%%time
# interact for 7 ticks
_, action_log, reward_log, _, _, _ = pool.interact(5)
print('actions:')
print(action_log[0])
print("rewards")
print(reward_log[0])
# load first sessions (this... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Q-learningWe train our agent based on sessions it has played in `pool.update(SEQ_LENGTH)`To do so, we first obtain sequences of observations, rewards, actions, q-values, etc.Actions and rewards have shape `[n_games,seq_length]`, q-values are `[n_games,seq_length,n_actions]` | # get agent's Qvalues obtained via experience replay
replay = pool.experience_replay
actions, rewards, is_alive = replay.actions[0], replay.rewards, replay.is_alive
_, _, _, _, qvalues = agent.get_sessions(
replay,
session_length=SEQ_LENGTH,
experience_replay=True,
)
assert actions.ndim == rewards.ndim =... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Demo runas usual... | action_layer.epsilon.set_value(0.05)
untrained_reward = np.mean(pool.evaluate(save_path="./records",
record_video=True))
# show video
from IPython.display import HTML
import os
video_names = list(
filter(lambda s: s.endswith(".mp4"), os.listdir("./records/")))
HTML("""
<vi... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Training loop | # starting epoch
epoch_counter = 1
# full game rewards
rewards = {}
loss, reward_per_tick, reward = 0, 0, 0
from tqdm import trange
from IPython.display import clear_output
for i in trange(150000):
# update agent's epsilon (in e-greedy policy)
current_epsilon = 0.05 + 0.45*np.exp(-epoch_counter/20000.)
... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Evaluating results * Here we plot learning curves and sample testimonials | import pandas as pd
plt.plot(*zip(*sorted(rewards.items(), key=lambda k: k[0])))
from agentnet.utils.persistence import save, load
save(action_layer, "pacman.pcl")
action_layer.epsilon.set_value(0.05)
rw = pool.evaluate(n_games=20, save_path="./records", record_video=False)
print("mean session score=%f.5" % np.mean(rw)... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
Assignment part I (5 pts)We'll start by implementing target network to stabilize training.There are two ways to do so: __1)__ Manually write lasagne network, or clone it via [one of those methods](https://github.com/Lasagne/Lasagne/issues/720).You will need to implement loading weights from original network to target ... | from lasagne.layers import *
class DuelingQvaluesLayer(MergeLayer):
def get_output_for(self, inputs, **tags):
V, A = inputs
return <YOUR CODE: add them up :)>
def get_output_shape_for(self, input_shapes, **tags):
V_shape, A_shape=input_shapes
assert len(
V_shape) =... | _____no_output_____ | Unlicense | week04_approx_rl/homework_lasagne.ipynb | Kirili4ik/Practical_RL |
__mlmachine - GroupbyImputer, KFoldEncoder, and Skew Correction__Welcome to Example Notebook 2. If you're new to mlmachine, check out [Example Notebook 1](https://github.com/petersontylerd/mlmachine/blob/master/notebooks/mlmachine_part_1.ipynb).Check out the [GitHub repository](https://github.com/petersontylerd/mlmachi... | # import libraries
import numpy as np
import pandas as pd
# import mlmachine tools
import mlmachine as mlm
from mlmachine.data import titanic
# use titanic() function to create DataFrames for training and validation datasets
df_train, df_valid = titanic()
# ordinal encoding hierarchy
ordinal_encodings = {"Pclass": [... | ~/.pyenv/versions/main37/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when ... | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
--- Assessment---Each `Machine()` object contains a method for summarizing missingness in tabular form and in graphical form: | # generate missingness summary for training data
mlmachine_titanic_train.eda_missing_summary(display_df=True) | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---By default, this method acts on the `data` attribute associated with `mlmachine_train`. Let's do the same for the validation dataset: | # generate missingness summary for validation data
mlmachine_titanic_valid.eda_missing_summary(display_df=True) | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---Next, we need to determine if there are features with missing values in the training data, but not the validation data, and vice versa. This informs how we should set up our transformation pipeline. For example, if a feature has missing values in the validation dataset, but not the training dataset, we will still wa... | # generate missingness comparison summary
mlmachine_titanic_train.missing_column_compare(
validation_data=mlmachine_titanic_valid.data,
) | Feature has missing values in validation data, not training data.
{'Fare'}
Feature has missing values in training data, not validation data.
{'Embarked'}
| MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---The key observation here is that "Fare" is fully populated in the training data, but not the validation data. We need to make sure our pipeline learns how to impute these missing values based on the training data, despite the fact that the training data is not missing any values in this feature. --- GroupbyImputer--... | # import mlmachine tools
from mlmachine.features.preprocessing import GroupbyImputer
# instantiate GroupbyImputer to fill "Age" mean, grouped by "SibSp"
impute = GroupbyImputer(null_column="Age", groupby_column="SibSp", strategy="mean")
impute.fit_transform(mlmachine_titanic_train.data[["Age","SibSp"]])
display(impute... | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---In the code snippet above, we mean impute "Age", grouped by "SibSp". We pass "Age" to the `null_column` parameter to indicate which column contains the nulls, and pass "SibSp" to the `groupby_column` parameter. The strategy parameter receives the same instructions as Scikit-learn's `SimpleImputer()` - "mean", "media... | # import libraries
from sklearn.impute import SimpleImputer
from sklearn.pipeline import make_pipeline
# import mlmachine tools
from mlmachine.features.preprocessing import (
DataFrameSelector,
PandasTransformer,
PandasFeatureUnion,
)
# create imputation PandasFeatureUnion pipeline
impute_pipe = PandasFea... | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---`GroupbyImputer()` makes two appearances in this `PandasFeatureUnion()` operation. On line 4, we groupby the feature "SibSp" to impute the mean "Age" value, and on line 8 we groupby the feature "Pclass" to impute the mean "Fare" value. Imputations for "Embarked" and "Cabin" are completed in straightforward fashion -... | # import libraries
from sklearn.preprocessing import KBinsDiscretizer
# create simple encoding PandasFeatureUnion pipeline
encode_pipe = PandasFeatureUnion([
("bin", make_pipeline(
DataFrameSelector(include_columns=["Age"]),
PandasTransformer(KBinsDiscretizer(encode="ordinal"))
)),
("select... | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---This operation returns a binned version of "Age", as well as the original "Age" and "Pclass" features. | mlmachine_titanic_train.data[:10] | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---Next, we target encode both "Pclass" and "Age_binned_5" using mean encoding, CatBoost encoding and Weight of Evidence encoding as provided by the package category_encoders. | # import libraries
from sklearn.model_selection import KFold
from category_encoders import WOEEncoder, TargetEncoder, CatBoostEncoder
# import mlmachine tools
from mlmachine.features.preprocessing import KFoldEncoder
# create KFold encoding PandasFeatureUnion pipeline
target_encode_pipe = PandasFeatureUnion([
("t... | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---Let's review the key `KFoldEncoder()` parameters:- `target`: the target attribute of our mlmachine_titanic_train object- `cv`: a cross-validation object- `encoder`: a target encoder class`KFoldEncoder()` learns the encoded values on the training data, and applies the values to the out-of-fold observations. On the va... | # generate skewness summary
mlmachine_titanic_train.skew_summary() | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---The `skew_summary()` method returns a `DataFrame` that summarizes the skew for each feature, along with a "Percent zero" column, which informs us of the percentage of values in the feature that are zero. --- Skew correction---mlmachine contains a class called `DualTransformer()`, which, by default, applies both Yeo-... | # import mlmachine tools
from mlmachine.features.preprocessing import DualTransformer
# create skew correction PandasFeatureUnion pipeline
skew_pipe = PandasFeatureUnion([
("skew", make_pipeline(
DataFrameSelector(include_columns=["Age"]),
DualTransformer(),
)),
])
# fit and transform trai... | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
---`DualTransformer()` adds the features "Age_BoxCox" and "Age_YeoJohnson". Let's execute `skew_summary()` again to see if `DualTransformer()` addressed the skew in our original feature:"Age_BoxCox" and "Age_YeoJohnson" have a skew of 0.0286 and 0.0483, respectively. | # generate skewness summary
mlmachine_titanic_train.skew_summary() | _____no_output_____ | MIT | notebooks/mlmachine_part_2.ipynb | klahrich/mlmachine |
Tools to analyze the results of Gate simulations | #hide
from nbdev.showdoc import * | _____no_output_____ | Apache-2.0 | nbs/01_analysis.ipynb | dmitryhits/ProtonBeamTherapy |
Dependencies | #export
import pandas as pd
import uproot as rt
import awkward as ak
from scipy.stats import moyal
import matplotlib.pyplot as plt
import numpy as np
import math
from scipy import stats
from scipy.stats import rv_continuous
# import pylandau
from matplotlib.pyplot import hist2d
import matplotlib.colors as mcolors
impo... | _____no_output_____ | Apache-2.0 | nbs/01_analysis.ipynb | dmitryhits/ProtonBeamTherapy |
ph0 = get_phasespace_df('2021May03_141349', 0)ph5 = get_phasespace_df('2021May03_141349', 5)ph10 = get_phasespace_df('2021May03_141349', 10)ph15 = get_phasespace_df('2021May03_141349', 15)ph19 = get_phasespace_df('2021May03_141349', 19) | get_Ekin(ph0).hist(bins=50)
get_Ekin(ph19).hist(bins=50)
print(round(stats.tstd(get_Ekin(ph0), (175, 200)), 2), 'MeV')
print(round(stats.tstd(get_Ekin(ph19), (55, 90)), 2), 'MeV')
from matplotlib import colors
x0 = ph19[(ph19["ParticleName"]=='proton') & (ph19['CreatorProcess'] == 0) & (ph19['NuclearProcess'] == 0)]['... | _____no_output_____ | Apache-2.0 | nbs/01_analysis.ipynb | dmitryhits/ProtonBeamTherapy |
Lateral positions of protons in the last phantom layer. On the left are all protons, on the right are only the primary protons that did not experience nuclear scattering. | x0 = ph19[(ph19["ParticleName"]=='proton') & (ph19['CreatorProcess'] == 0) & (ph19['NuclearProcess'] == 0)]['X']
ekin0 = ph19[(ph19["ParticleName"]=='proton') & (ph19['CreatorProcess'] == 0) & (ph19['NuclearProcess'] == 0)]['Ekine']
x1 = ph19[(ph19["ParticleName"]=='proton')]['X']
ekin1 = ph19[(ph19["ParticleName"]... | _____no_output_____ | Apache-2.0 | nbs/01_analysis.ipynb | dmitryhits/ProtonBeamTherapy |
Kinetic energy deposited by particle versus the position of the hit (left) all protons (right) protons from the primary beam that did not experience nuclear scattering | ph = pd.merge(ph0, ph5, on="EventID", suffixes=("", "_5"))
ph = pd.merge(ph, ph10, on="EventID", suffixes=("", "_10"))
ph = pd.merge(ph, ph15, on="EventID", suffixes=("", "_15"))
ph = pd.merge(ph, ph19, on="EventID", suffixes=("", "_19"))
def select(ph):
result = (ph[f"CreatorProcess"] == 0) & (ph[f"ParticleName"] ... | _____no_output_____ | Apache-2.0 | nbs/01_analysis.ipynb | dmitryhits/ProtonBeamTherapy |
Example showing energy deposition with 3 sensors | df2 = get_df_subentry2('results/TrackerHits.root')
edep = get_edep_data(df2, sensor=0)
_ = plt.hist(edep, bins=100, range=(0,1000))
_ = plt.hist(get_edep_data(df2, sensor=1), bins=100, range=(0,1000))
_ = plt.hist(get_edep_data(df2, sensor=2), bins=100, range=(0,1000))
null_columns = [col for col in df2.columns if df2[... | _____no_output_____ | Apache-2.0 | nbs/01_analysis.ipynb | dmitryhits/ProtonBeamTherapy |
序列到序列学习（seq2seq）在`seq2seq`中，特定的“<eos>”表示序列结束词元。一旦输出序列生成此词元，模型就会停止预测。在循环神经网络解码器的初始化时间步，有两个特定的设计决定：首先，特定的“<bos>”表示序列开始词元，它是解码器的输入序列的第一个词元。其次，使用循环神经网络编码器最终的隐状态来初始化解码器的隐状态。这种设计将输入序列的编码信息送入到解码器中来生成输出序列的。在其他一些设计中 :cite:`Cho.Van-Merrienboer.Gulcehre.ea.2014`，编码器最终的隐状态在每一个时间步都作为解码器的输入序列的一部分。类似于 :`language_model`中... | import collections
import math
import tensorflow as tf
from d2l import tensorflow as d2l | _____no_output_____ | MIT | 09_recurrent_neural_networks/tf_seq2seq.ipynb | JiaheXu/Machine-Learning-Codebase |
编码器从技术上讲，编码器将长度可变的输入序列转换成形状固定的上下文变量$\mathbf{c}$，并且将输入序列的信息在该上下文变量中进行编码。如 :numref:`fig_seq2seq`所示，可以使用循环神经网络来设计编码器。考虑由一个序列组成的样本（批量大小是$1$）。假设输入序列是$x_1, \ldots, x_T$，其中$x_t$是输入文本序列中的第$t$个词元。在时间步$t$，循环神经网络将词元$x_t$的输入特征向量$\mathbf{x}_t$和$\mathbf{h} _{t-1}$（即上一时间步的隐状态）转换为$\mathbf{h}_t$（即当前步的隐状态）。使用一个函数$f$来描述循环神经网络的循环层所做的变换：$... | class Encoder(tf.keras.layers.Layer):
"""编码器-解码器架构的基本编码器接口"""
def __init__(self, **kwargs):
super(Encoder, self).__init__(**kwargs)
def call(self, X, *args, **kwargs):
raise NotImplementedError
#@save
class Seq2SeqEncoder(d2l.Encoder):
"""用于序列到序列学习的循环神经网络编码器"""
def __init__(self, vo... | _____no_output_____ | MIT | 09_recurrent_neural_networks/tf_seq2seq.ipynb | JiaheXu/Machine-Learning-Codebase |
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