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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4. Visualize the resultsLet's take a look: | for i in range(1, len(average_precisions)):
print("{:<14}{:<6}{}".format(classes[i], 'AP', round(average_precisions[i], 3)))
print()
print("{:<14}{:<6}{}".format('','mAP', round(mean_average_precision, 3)))
m = max((n_classes + 1) // 2, 2)
n = 2
fig, cells = plt.subplots(m, n, figsize=(n*8,m*8))
for i in range(m):... | _____no_output_____ | Apache-2.0 | ssd300_evaluation.ipynb | rogeryan/ssd_keras |
5. Advanced use`Evaluator` objects maintain copies of all relevant intermediate results like predictions, precisions and recalls, etc., so in case you want to experiment with different parameters, e.g. different IoU overlaps, there is no need to compute the predictions all over again every time you make a change to a ... | evaluator.get_num_gt_per_class(ignore_neutral_boxes=True,
verbose=False,
ret=False)
evaluator.match_predictions(ignore_neutral_boxes=True,
matching_iou_threshold=0.5,
border_pixels='include',
... | aeroplane AP 0.822
bicycle AP 0.874
bird AP 0.787
boat AP 0.713
bottle AP 0.505
bus AP 0.899
car AP 0.89
cat AP 0.923
chair AP 0.61
cow AP 0.845
diningtable AP 0.79
dog AP 0.899
horse ... | Apache-2.0 | ssd300_evaluation.ipynb | rogeryan/ssd_keras |
Roboschool simulations of physical robotics with Amazon SageMaker--- IntroductionRoboschool is an [open source](https://github.com/openai/roboschool/tree/master/roboschool) physics simulator that is commonly used to train RL policies for simulated robotic systems. Roboschool provides 3D visualization of physical syst... | # Uncomment the problem to work on
roboschool_problem = "reacher"
# roboschool_problem = 'hopper'
# roboschool_problem = 'humanoid' | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Pre-requisites ImportsTo get started, we'll import the Python libraries we need, set up the environment with a few prerequisites for permissions and configurations. | import sagemaker
import boto3
import sys
import os
import glob
import re
import subprocess
import numpy as np
from IPython.display import HTML
import time
from time import gmtime, strftime
sys.path.append("common")
from misc import get_execution_role, wait_for_s3_object
from docker_utils import build_and_push_docker_i... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Setup S3 bucketSet up the linkage and authentication to the S3 bucket that you want to use for checkpoint and the metadata. | sage_session = sagemaker.session.Session()
s3_bucket = sage_session.default_bucket()
s3_output_path = "s3://{}/".format(s3_bucket)
print("S3 bucket path: {}".format(s3_output_path)) | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Define Variables We define variables such as the job prefix for the training jobs *and the image path for the container (only when this is BYOC).* | # create a descriptive job name
job_name_prefix = "rl-roboschool-" + roboschool_problem | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Configure where training happensYou can train your RL training jobs using the SageMaker notebook instance or local notebook instance. In both of these scenarios, you can run the following in either local or SageMaker modes. The local mode uses the SageMaker Python SDK to run your code in a local container before deplo... | # run in local_mode on this machine, or as a SageMaker TrainingJob?
local_mode = False
if local_mode:
instance_type = "local"
else:
# If on SageMaker, pick the instance type
instance_type = "ml.c5.2xlarge" | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Create an IAM roleEither get the execution role when running from a SageMaker notebook instance `role = sagemaker.get_execution_role()` or, when running from local notebook instance, use utils method `role = get_execution_role()` to create an execution role. | try:
role = sagemaker.get_execution_role()
except:
role = get_execution_role()
print("Using IAM role arn: {}".format(role)) | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Install docker for `local` modeIn order to work in `local` mode, you need to have docker installed. When running from you local machine, please make sure that you have docker and docker-compose (for local CPU machines) and nvidia-docker (for local GPU machines) installed. Alternatively, when running from a SageMaker n... | # only run from SageMaker notebook instance
if local_mode:
!/bin/bash ./common/setup.sh | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Build docker containerWe must build a custom docker container with Roboschool installed. This takes care of everything:1. Fetching base container image2. Installing Roboschool and its dependencies3. Uploading the new container image to ECRThis step can take a long time if you are running on a machine with a slow inte... | %%time
cpu_or_gpu = "gpu" if instance_type.startswith("ml.p") else "cpu"
repository_short_name = "sagemaker-roboschool-ray-%s" % cpu_or_gpu
docker_build_args = {
"CPU_OR_GPU": cpu_or_gpu,
"AWS_REGION": boto3.Session().region_name,
}
custom_image_name = build_and_push_docker_image(repository_short_name, build_a... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Write the Training CodeThe training code is written in the file “train-coach.py” which is uploaded in the /src directory. First import the environment files and the preset files, and then define the main() function. | !pygmentize src/train-{roboschool_problem}.py | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Train the RL model using the Python SDK Script modeIf you are using local mode, the training will run on the notebook instance. When using SageMaker for training, you can select a GPU or CPU instance. The RLEstimator is used for training RL jobs. 1. Specify the source directory where the environment, presets and train... | %%time
metric_definitions = RLEstimator.default_metric_definitions(RLToolkit.RAY)
estimator = RLEstimator(
entry_point="train-%s.py" % roboschool_problem,
source_dir="src",
dependencies=["common/sagemaker_rl"],
image_uri=custom_image_name,
role=role,
instance_type=instance_type,
instance_c... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
VisualizationRL training can take a long time. So while it's running there are a variety of ways we can track progress of the running training job. Some intermediate output gets saved to S3 during training, so we'll set up to capture that. | print("Job name: {}".format(job_name))
s3_url = "s3://{}/{}".format(s3_bucket, job_name)
intermediate_folder_key = "{}/output/intermediate/".format(job_name)
intermediate_url = "s3://{}/{}".format(s3_bucket, intermediate_folder_key)
print("S3 job path: {}".format(s3_url))
print("Intermediate folder path: {}".format(... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Fetch videos of training rolloutsVideos of certain rollouts get written to S3 during training. Here we fetch the last 10 videos from S3, and render the last one. | recent_videos = wait_for_s3_object(
s3_bucket,
intermediate_folder_key,
tmp_dir,
fetch_only=(lambda obj: obj.key.endswith(".mp4") and obj.size > 0),
limit=10,
training_job_name=job_name,
)
last_video = sorted(recent_videos)[-1] # Pick which video to watch
os.system("mkdir -p ./src/tmp_render/ &... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Plot metrics for training jobWe can see the reward metric of the training as it's running, using algorithm metrics that are recorded in CloudWatch metrics. We can plot this to see the performance of the model over time. | %matplotlib inline
from sagemaker.analytics import TrainingJobAnalytics
if not local_mode:
df = TrainingJobAnalytics(job_name, ["episode_reward_mean"]).dataframe()
num_metrics = len(df)
if num_metrics == 0:
print("No algorithm metrics found in CloudWatch")
else:
plt = df.plot(x="timesta... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Monitor training progressYou can repeatedly run the visualization cells to get the latest videos or see the latest metrics as the training job proceeds. Evaluation of RL modelsWe use the last checkpointed model to run evaluation for the RL Agent. Load checkpointed modelCheckpointed data from the previously trained m... | if local_mode:
model_tar_key = "{}/model.tar.gz".format(job_name)
else:
model_tar_key = "{}/output/model.tar.gz".format(job_name)
local_checkpoint_dir = "{}/model".format(tmp_dir)
wait_for_s3_object(s3_bucket, model_tar_key, tmp_dir, training_job_name=job_name)
if not os.path.isfile("{}/model.tar.gz".format(... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Visualize the output Optionally, you can run the steps defined earlier to visualize the output. Model deploymentNow let us deploy the RL policy so that we can get the optimal action, given an environment observation. | from sagemaker.tensorflow.model import TensorFlowModel
model = TensorFlowModel(model_data=estimator.model_data, framework_version="2.1.0", role=role)
predictor = model.deploy(initial_instance_count=1, instance_type=instance_type)
# Mapping of environments to observation space
observation_space_mapping = {"reacher": 9... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Now let us predict the actions using a dummy observation | # ray 0.8.2 requires all the following inputs
# 'prev_action', 'is_training', 'prev_reward' and 'seq_lens' are placeholders for this example
# they won't affect prediction results
input = {
"inputs": {
"observations": np.ones(shape=(1, observation_space_mapping[roboschool_problem])).tolist(),
"prev... | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Clean up endpoint | predictor.delete_endpoint() | _____no_output_____ | Apache-2.0 | reinforcement_learning/rl_roboschool_ray/rl_roboschool_ray.ipynb | Amirosimani/amazon-sagemaker-examples |
Plotting Asteroids | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd | _____no_output_____ | MIT | HW_Plotting.ipynb | UWashington-Astro300/Astro300-A21 |
Imports | import pandas as pd
import numpy as np
pd.set_option('display.max_colwidth', None)
import re
from wordcloud import WordCloud
import contractions
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
plt.rcParams['font.size'] = 15
import nltk
from nltk.stem.porter import PorterStemmer
from n... | _____no_output_____ | MIT | Week 1/3_Natural_Disaster_Tweets_EDA_Visuals.ipynb | theaslo/NLP_Workshop_WAIA_Sept_2021 |
Data Load | df_train = pd.read_csv('../Datasets/disaster_tweet/train.csv')
df_train.head(20)
df_train.tail(20) | _____no_output_____ | MIT | Week 1/3_Natural_Disaster_Tweets_EDA_Visuals.ipynb | theaslo/NLP_Workshop_WAIA_Sept_2021 |
Observation1. Mixed case2. Contractions3. Hashtags and mentions4. Incorrect spellings5. Punctuations6. websites and urls Functions | all_text = ' '.join(list(df_train['text']))
def check_texts(check_item, all_text):
return check_item in all_text
print(check_texts('<a', all_text))
print(check_texts('<div', all_text))
print(check_texts('<p', all_text))
print(check_texts('#x', all_text))
print(check_texts(':)', all_text))
print(check_texts('<3', a... | _____no_output_____ | MIT | Week 1/3_Natural_Disaster_Tweets_EDA_Visuals.ipynb | theaslo/NLP_Workshop_WAIA_Sept_2021 |
Tweet Length Analysis | sns.histplot(x='words_per_tweet', hue='target', data=df_train, kde=True)
plt.show() | _____no_output_____ | MIT | Week 1/3_Natural_Disaster_Tweets_EDA_Visuals.ipynb | theaslo/NLP_Workshop_WAIA_Sept_2021 |
Punctuation Analysis | sns.countplot(x='target', hue='punctuation_count', data=df_train)
plt.legend([])
plt.show() | _____no_output_____ | MIT | Week 1/3_Natural_Disaster_Tweets_EDA_Visuals.ipynb | theaslo/NLP_Workshop_WAIA_Sept_2021 |
Tweet Text Analysis using WordCloud | real_disaster_tweets = ' '.join(list(df_train[df_train['target'] == 1]['text']))
real_disaster_tweets
non_real_disaster_tweets = ' '. join(list(df_train[df_train['target'] == 0]['text']))
wc = WordCloud(background_color="black",
max_words=100,
width=1000,
height=600,
... | _____no_output_____ | MIT | Week 1/3_Natural_Disaster_Tweets_EDA_Visuals.ipynb | theaslo/NLP_Workshop_WAIA_Sept_2021 |
For both the parties the proportion of negative tweets is slightly greater than the positive tweets. Let's create a Word Cloud to identify which words occur frequetly in the tweets and try to derive what is their significance. | bjp_tweets = bjp_df['clean_text']
bjp_string =[]
for t in bjp_tweets:
bjp_string.append(t)
bjp_string = pd.Series(bjp_string).str.cat(sep=' ')
from wordcloud import WordCloud
wordcloud = WordCloud(width=1600, height=800,max_font_size=200).generate(bjp_string)
plt.figure(figsize=(12,10))
plt.title('BJP Word Cloud')... | _____no_output_____ | MIT | Exploratory Data Analysis.ipynb | abhishek291994/Twitter-Sentiment-Analysis-for-Indian-Elections |
The words like 'JhaSanjay', 'ArvindKejriwal', 'Delhi', 'Govindraj' occur frequently in our corpus and are highlighted by our Word Cloud.In the context of the BJP. Arivind Kejriwal are staunch opponents of the BJP government. Delhi is the Capital of India and also a state that the BJP has suffered heavy losses in the pr... | cong_tweets = congress_df['clean_text']
cong_string =[]
for t in cong_tweets:
cong_string.append(t)
cong_string = pd.Series(cong_string).str.cat(sep=' ')
from wordcloud import WordCloud
wordcloud = WordCloud(width=1600, height=800,max_font_size=200).generate(cong_string)
plt.figure(figsize=(12,10))
plt.title('Cong... | _____no_output_____ | MIT | Exploratory Data Analysis.ipynb | abhishek291994/Twitter-Sentiment-Analysis-for-Indian-Elections |
rocket functions> ROCKET (RandOm Convolutional KErnel Transform) functions for univariate and multivariate time series using GPU. | #export
from tsai.imports import *
from tsai.data.external import *
#export
from sklearn.linear_model import RidgeClassifierCV
from numba import njit, prange
#export
# Angus Dempster, Francois Petitjean, Geoff Webb
# Dempster A, Petitjean F, Webb GI (2019) ROCKET: Exceptionally fast and
# accurate time series classifi... | _____no_output_____ | Apache-2.0 | nbs/010_rocket_functions.ipynb | williamsdoug/timeseriesAI |
DictionariesWe've been learning about *sequences* in Python but now we're going to switch gears and learn about *mappings* in Python. If you're familiar with other languages you can think of these Dictionaries as hash tables. This section will serve as a brief introduction to dictionaries and consist of: 1.) Constr... | # Make a dictionary with {} and : to signify a key and a value
my_dict = {'key1':'value1','key2':'value2'}
# Call values by their key
my_dict['key2'] | _____no_output_____ | MIT | Languages/Python/00 - Python Object and Data Structure/05-Dictionaries.ipynb | Tikam02/ReBuild |
Its important to note that dictionaries are very flexible in the data types they can hold. For example: | my_dict = {'key1':123,'key2':[12,23,33],'key3':['item0','item1','item2']}
# Let's call items from the dictionary
my_dict['key3']
# Can call an index on that value
my_dict['key3'][0]
# Can then even call methods on that value
my_dict['key3'][0].upper() | _____no_output_____ | MIT | Languages/Python/00 - Python Object and Data Structure/05-Dictionaries.ipynb | Tikam02/ReBuild |
We can affect the values of a key as well. For instance: | my_dict['key1']
# Subtract 123 from the value
my_dict['key1'] = my_dict['key1'] - 123
#Check
my_dict['key1'] | _____no_output_____ | MIT | Languages/Python/00 - Python Object and Data Structure/05-Dictionaries.ipynb | Tikam02/ReBuild |
A quick note, Python has a built-in method of doing a self subtraction or addition (or multiplication or division). We could have also used += or -= for the above statement. For example: | # Set the object equal to itself minus 123
my_dict['key1'] -= 123
my_dict['key1'] | _____no_output_____ | MIT | Languages/Python/00 - Python Object and Data Structure/05-Dictionaries.ipynb | Tikam02/ReBuild |
We can also create keys by assignment. For instance if we started off with an empty dictionary, we could continually add to it: | # Create a new dictionary
d = {}
# Create a new key through assignment
d['animal'] = 'Dog'
# Can do this with any object
d['answer'] = 42
#Show
d | _____no_output_____ | MIT | Languages/Python/00 - Python Object and Data Structure/05-Dictionaries.ipynb | Tikam02/ReBuild |
Nesting with DictionariesHopefully you're starting to see how powerful Python is with its flexibility of nesting objects and calling methods on them. Let's see a dictionary nested inside a dictionary: | # Dictionary nested inside a dictionary nested inside a dictionary
d = {'key1':{'nestkey':{'subnestkey':'value'}}} | _____no_output_____ | MIT | Languages/Python/00 - Python Object and Data Structure/05-Dictionaries.ipynb | Tikam02/ReBuild |
Wow! That's a quite the inception of dictionaries! Let's see how we can grab that value: | # Keep calling the keys
d['key1']['nestkey']['subnestkey'] | _____no_output_____ | MIT | Languages/Python/00 - Python Object and Data Structure/05-Dictionaries.ipynb | Tikam02/ReBuild |
A few Dictionary MethodsThere are a few methods we can call on a dictionary. Let's get a quick introduction to a few of them: | # Create a typical dictionary
d = {'key1':1,'key2':2,'key3':3}
# Method to return a list of all keys
d.keys()
# Method to grab all values
d.values()
# Method to return tuples of all items (we'll learn about tuples soon)
d.items() | _____no_output_____ | MIT | Languages/Python/00 - Python Object and Data Structure/05-Dictionaries.ipynb | Tikam02/ReBuild |
Measures of Income Mobility **Author: Wei Kang , Serge Rey **Income mobility could be viewed as a reranking pheonomenon where regions switch income positions while it could also be considered to be happening as long as regions move away from the previous income levels. The former is named absolute mobility and the lat... | from giddy import markov,mobility
mobility.markov_mobility? | _____no_output_____ | BSD-3-Clause | notebooks/Mobility measures.ipynb | mikiec84/giddy |
US income mobility exampleSimilar to [Markov Based Methods notebook](Markov Based Methods.ipynb), we will demonstrate the usage of the mobility methods by an application to data on per capita incomes observed annually from 1929 to 2009 for the lower 48 US states. | import libpysal
import numpy as np
import mapclassify as mc
income_path = libpysal.examples.get_path("usjoin.csv")
f = libpysal.io.open(income_path)
pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]) #each column represents an state's income time series 1929-2010
q5 = np.array([mc.Quantiles(y).yb for y in pc... | /Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in ... | BSD-3-Clause | notebooks/Mobility measures.ipynb | mikiec84/giddy |
After acquiring the estimate of transition probability matrix, we could call the method $markov\_mobility$ to estimate any of the five Markov-based summary mobility indice. 1. Shorrock1's mobility measure$$M_{P} = \frac{m-\sum_{i=1}^m P_{ii}}{m-1}$$```pythonmeasure = "P"``` | mobility.markov_mobility(m.p, measure="P") | _____no_output_____ | BSD-3-Clause | notebooks/Mobility measures.ipynb | mikiec84/giddy |
2. Shorroks2's mobility measure$$M_{D} = 1 - |\det(P)|$$```pythonmeasure = "D"``` | mobility.markov_mobility(m.p, measure="D") | _____no_output_____ | BSD-3-Clause | notebooks/Mobility measures.ipynb | mikiec84/giddy |
3. Sommers and Conlisk's mobility measure$$M_{L2} = 1 - |\lambda_2|$$```pythonmeasure = "L2"``` | mobility.markov_mobility(m.p, measure = "L2") | _____no_output_____ | BSD-3-Clause | notebooks/Mobility measures.ipynb | mikiec84/giddy |
4. Bartholomew1's mobility measure$$M_{B1} = \frac{m-m \sum_{i=1}^m \pi_i P_{ii}}{m-1}$$$\pi$: the inital income distribution```pythonmeasure = "B1"``` | pi = np.array([0.1,0.2,0.2,0.4,0.1])
mobility.markov_mobility(m.p, measure = "B1", ini=pi) | _____no_output_____ | BSD-3-Clause | notebooks/Mobility measures.ipynb | mikiec84/giddy |
5. Bartholomew2's mobility measure$$M_{B2} = \frac{1}{m-1} \sum_{i=1}^m \sum_{j=1}^m \pi_i P_{ij} |i-j|$$$\pi$: the inital income distribution```pythonmeasure = "B1"``` | pi = np.array([0.1,0.2,0.2,0.4,0.1])
mobility.markov_mobility(m.p, measure = "B2", ini=pi) | _____no_output_____ | BSD-3-Clause | notebooks/Mobility measures.ipynb | mikiec84/giddy |
FaceNet Keras DemoThis notebook demos the usage of the FaceNet model, and showshow to preprocess images before feeding them into the model. | %load_ext autoreload
%autoreload 2
import sys
sys.path.append('../')
import os
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from PIL import Image
from skimage.transform import resize
from skimage.util import img_as_ubyte, img_as_float
from sklearn.metrics import p... | _____no_output_____ | MIT | notebooks/demos/demo_embeddings.ipynb | psturmfels/adversarial_faces |
Loading the ModelFirst you need to download the keras weights from https://github.com/nyoki-mtl/keras-facenet and put the downloaded weights file in the parent directory. | model_path = '../facenet_keras.h5'
model = tf.keras.models.load_model(model_path) | WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.
| MIT | notebooks/demos/demo_embeddings.ipynb | psturmfels/adversarial_faces |
Preprocessing the InputThis next cell preprocesses the input using Pillow and skimage, both of which can be installed using pip. We center crop the image to avoid scaling issues, then resize the image to 160 x 160, and then we standardize the images using the utils module in this repository. | images = []
images_whitened = []
image_path = '../images/'
image_files = os.listdir(image_path)
image_files = [image_file for image_file in image_files if image_file.endswith('.png')]
for image_file in image_files:
image = np.array(Image.open(os.path.join(image_path, image_file)))
image = image[:, :, :3]
im... | _____no_output_____ | MIT | notebooks/demos/demo_embeddings.ipynb | psturmfels/adversarial_faces |
Computing EmbeddingsFinally, we compute the embeddings and pairwise distances of the images. We can see that the model is able to distinguish the same identity from different identities! | image_batch = tf.convert_to_tensor(np.array(images_whitened))
embedding_batch = model.predict(image_batch)
normalized_embedding_batch = l2_normalize(embedding_batch)
np.sqrt(np.sum(np.square(normalized_embedding_batch[0] - normalized_embedding_batch[1])))
pairwise_distance_matrix = pairwise_distances(normalized_embeddi... | _____no_output_____ | MIT | notebooks/demos/demo_embeddings.ipynb | psturmfels/adversarial_faces |
Temporal-Difference MethodsIn this notebook, you will write your own implementations of many Temporal-Difference (TD) methods.While we have provided some starter code, you are welcome to erase these hints and write your code from scratch.--- Part 0: Explore CliffWalkingEnvWe begin by importing the necessary packages. | import sys
import gym
import numpy as np
import random as rn
from collections import defaultdict, deque
import matplotlib.pyplot as plt
%matplotlib inline
import check_test
from plot_utils import plot_values | _____no_output_____ | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
Use the code cell below to create an instance of the [CliffWalking](https://github.com/openai/gym/blob/master/gym/envs/toy_text/cliffwalking.py) environment. | env = gym.make('CliffWalking-v0') | _____no_output_____ | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
The agent moves through a $4\times 12$ gridworld, with states numbered as follows:```[[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35], [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]]```At the start of any episode, sta... | print(env.action_space)
print(env.observation_space) | Discrete(4)
Discrete(48)
| MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
In this mini-project, we will build towards finding the optimal policy for the CliffWalking environment. The optimal state-value function is visualized below. Please take the time now to make sure that you understand _why_ this is the optimal state-value function._**Note**: You can safely ignore the values of the cli... | # define the optimal state-value function
V_opt = np.zeros((4,12))
V_opt[0:13][0] = -np.arange(3, 15)[::-1]
V_opt[0:13][1] = -np.arange(3, 15)[::-1] + 1
V_opt[0:13][2] = -np.arange(3, 15)[::-1] + 2
V_opt[3][0] = -13
plot_values(V_opt) | _____no_output_____ | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
Part 1: TD Control: SarsaIn this section, you will write your own implementation of the Sarsa control algorithm.Your algorithm has four arguments:- `env`: This is an instance of an OpenAI Gym environment.- `num_episodes`: This is the number of episodes that are generated through agent-environment interaction.- `alpha`... | def greedy_eps_action(Q, state, nA, eps):
if rn.random()> eps:
return np.argmax(Q[state])
else:
return rn.choice(np.arange(nA))
def sarsa(env, num_episodes, alpha, gamma=1.0, eps_start = 1, eps_decay = .95, eps_min = 1e-2):
# initialize action-value function (empty dictionary of arrays)
... | _____no_output_____ | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
Use the next code cell to visualize the **_estimated_** optimal policy and the corresponding state-value function. If the code cell returns **PASSED**, then you have implemented the function correctly! Feel free to change the `num_episodes` and `alpha` parameters that are supplied to the function. However, if you'd ... | # obtain the estimated optimal policy and corresponding action-value function
Q_sarsa = sarsa(env, 5000, .01)
# print the estimated optimal policy
policy_sarsa = np.array([np.argmax(Q_sarsa[key]) if key in Q_sarsa else -1 for key in np.arange(48)]).reshape(4,12)
check_test.run_check('td_control_check', policy_sarsa)
p... | Episode 5000/5000 | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
Part 2: TD Control: Q-learningIn this section, you will write your own implementation of the Q-learning control algorithm.Your algorithm has four arguments:- `env`: This is an instance of an OpenAI Gym environment.- `num_episodes`: This is the number of episodes that are generated through agent-environment interaction... | def q_learning(env, num_episodes, alpha, gamma=1.0, eps_start = 1, eps_decay = .95, eps_min = 1e-2):
# initialize empty dictionary of arrays
nA = env.action_space.n
Q = defaultdict(lambda: np.zeros(env.nA))
eps = eps_start
# loop over episodes
for i_episode in range(1, num_episodes+1):
#... | _____no_output_____ | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
Use the next code cell to visualize the **_estimated_** optimal policy and the corresponding state-value function. If the code cell returns **PASSED**, then you have implemented the function correctly! Feel free to change the `num_episodes` and `alpha` parameters that are supplied to the function. However, if you'd l... | # obtain the estimated optimal policy and corresponding action-value function
Q_sarsamax = q_learning(env, 5000, .01)
# print the estimated optimal policy
policy_sarsamax = np.array([np.argmax(Q_sarsamax[key]) if key in Q_sarsamax else -1 for key in np.arange(48)]).reshape((4,12))
check_test.run_check('td_control_chec... | Episode 5000/5000 | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
Part 3: TD Control: Expected SarsaIn this section, you will write your own implementation of the Expected Sarsa control algorithm.Your algorithm has four arguments:- `env`: This is an instance of an OpenAI Gym environment.- `num_episodes`: This is the number of episodes that are generated through agent-environment int... | def expected_sarsa(env, num_episodes, alpha, gamma=1.0, eps_start = 1, eps_decay = .9, eps_min = 1e-2):
# initialize empty dictionary of arrays
nA = env.action_space.n
Q = defaultdict(lambda: np.zeros(env.nA))
eps = eps_start
# loop over episodes
for i_episode in range(1, num_episodes+1):
... | _____no_output_____ | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
Use the next code cell to visualize the **_estimated_** optimal policy and the corresponding state-value function. If the code cell returns **PASSED**, then you have implemented the function correctly! Feel free to change the `num_episodes` and `alpha` parameters that are supplied to the function. However, if you'd ... | # obtain the estimated optimal policy and corresponding action-value function
Q_expsarsa = expected_sarsa(env, 10000, 1)
# print the estimated optimal policy
policy_expsarsa = np.array([np.argmax(Q_expsarsa[key]) if key in Q_expsarsa else -1 for key in np.arange(48)]).reshape(4,12)
check_test.run_check('td_control_che... | Episode 10000/10000 | MIT | temporal-difference/Temporal_Difference.ipynb | kdliaokueida/Deep_Reinforcement_Learning |
_____no_output_____ | Apache-2.0 | w1d1_Primer_Notebook_de_Jupyter_en_GoogleColab_120521.ipynb | talitacardenas/The_Bridge_School_DataScience_PT | ||
Encabezado Encabezado tipo 2Se llama ***Markdown***1. Elemento de lista2. Elemento de lista En Jupyter oara ejecutar una celda tecleamos>- Ctrl + EnterO si queremos anadir una nueva línea de codigo y a la vez ejecutar- Alt + Enter | # Si quiero escribir un cmentario en una celda utilizamos el fragmento #
# Si sin más líneas también
| _____no_output_____ | Apache-2.0 | w1d1_Primer_Notebook_de_Jupyter_en_GoogleColab_120521.ipynb | talitacardenas/The_Bridge_School_DataScience_PT |
30 | 1 + 1
print("Es resultado de una operacion es:" + suma)
| _____no_output_____ | Apache-2.0 | w1d1_Primer_Notebook_de_Jupyter_en_GoogleColab_120521.ipynb | talitacardenas/The_Bridge_School_DataScience_PT |
Veamos que nos devolverá un error porque estamos intentando imorimir una cadena de texto y un valor numericoLa solucion seria trnsformar nuestro valor numerico en String | print("El resultado de esta operacion es> + str (suma)")
| _____no_output_____ | Apache-2.0 | w1d1_Primer_Notebook_de_Jupyter_en_GoogleColab_120521.ipynb | talitacardenas/The_Bridge_School_DataScience_PT |
作業在鐵達尼資料集中，今天我們專注觀察變數之間的相關性，以Titanic_train.csv 中，首先將有遺失值的數值刪除，並回答下列問題。* Q1: 透過數值法計算 Age 和 Survived 是否有相關性?* Q2:透過數值法計算 Sex 和 Survived 是否有相關性?* Q3: 透過數值法計算 Age 和 Fare 是否有相關性? * 提示: 1.產稱一個新的變數 Survived_cate ，資料型態傳換成類別型態 2.把題目中的 Survived 用 Survived_cate 來做分析 3.首先觀察一下這些變數的資料型態後，再來想要以哪一種判斷倆倆的相關性。 ... | # import library
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import math
import statistics
import seaborn as sns
from IPython.display import display
import pingouin as pg
import researchpy
%matplotlib inline | _____no_output_____ | MIT | Solution/Day_38_Solution.ipynb | YenLinWu/DataScienceMarathon |
讀入資料 | df_train = pd.read_csv("Titanic_train.csv")
print(df_train.info())
## 這邊我們做一個調整，把 Survived 變成離散型變數 Survived_cate
df_train['Survived_cate']=df_train['Survived']
df_train['Survived_cate']=df_train['Survived_cate'].astype('object')
print(df_train.info())
display(df_train.head(5)) | _____no_output_____ | MIT | Solution/Day_38_Solution.ipynb | YenLinWu/DataScienceMarathon |
Q1: 透過數值法計算 Age 和 Survived 是否有相關性? | ## Age:連續型 Survived_cate 為離散型，所以採用 Eta Squared | _____no_output_____ | MIT | Solution/Day_38_Solution.ipynb | YenLinWu/DataScienceMarathon |
計算相關係數，不能允許有遺失值，所以必須先補值，或者把遺失值刪除 | ## 取出資料後，把遺失值刪除
complete_data=df_train[['Age','Survived_cate']].dropna()
display(complete_data)
aov = pg.anova(dv='Age', between='Survived_cate', data=complete_data, detailed=True)
aov
etaSq = aov.SS[0] / (aov.SS[0] + aov.SS[1])
etaSq
def judgment_etaSq(etaSq):
if etaSq < .01:
qual = 'Negligible'
elif e... | _____no_output_____ | MIT | Solution/Day_38_Solution.ipynb | YenLinWu/DataScienceMarathon |
結論: 年紀和存活沒有相關性(complete_data)，思考是否需要放入模型，或者要深入觀察特性，是否需要做特徵轉換 Q2:透過數值法計算 Sex 和 Survived 是否有相關性? | ## Sex:離散型 Survived_cate 為離散型，所以採用 Cramér's V
contTable = pd.crosstab(df_train['Sex'], df_train['Survived_cate'])
contTable
df = min(contTable.shape[0], contTable.shape[1]) - 1
df
crosstab, res = researchpy.crosstab(df_train['Survived_cate'], df_train['Sex'], test='chi-square')
#print(res)
print("Cramer's value is",res... | _____no_output_____ | MIT | Solution/Day_38_Solution.ipynb | YenLinWu/DataScienceMarathon |
數值型態和圖形， 存活和性別存在高度的相關性，要預測存活，一定要把性別加上去。 Q3: 透過數值法計算 Age 和 Fare 是否有相關性? | ## Age 連續 , Fare 連續，用 Pearson 相關係數
## 取出資料後，把遺失值刪除
complete_data=df_train[['Age','Fare']].dropna()
display(complete_data)
# 由於 pearsonr 有兩個回傳結果，我們只需取第一個回傳值為相關係數
corr, _=stats.pearsonr(complete_data['Age'],complete_data['Fare'])
print(corr)
#代表身高和體重有高度線性相關
g = sns.regplot(x="Age", y="Fare", color="g",data=complete_data)... | _____no_output_____ | MIT | Solution/Day_38_Solution.ipynb | YenLinWu/DataScienceMarathon |
Imports | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, validation_curve
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, confusion_matrix, classification... | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Loading the data | df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.head()
df.shape | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
as we can see this data this data is about 303 rows and 14 column Exploring our dataset | df.sex.value_counts(normalize=True) | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
this mean we have more female then male. let's plot only people who got disease by sex | df.sex[df.target==1].value_counts().plot(kind="bar")
# commenting the plot
plt.title("people who got disease by sex")
plt.xlabel("sex")
plt.ylabel("effected");
plt.xticks(rotation = 0);
df.target.value_counts(normalize=True) | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
the two classes are almost equal Ploting Heart Disease by Age / Max Heart Rate | sns.scatterplot(x=df.age, y=df.thalach, hue = df.target);
# commenting the plot
plt.title("Heart Disease by Age / Max Heart Rate")
plt.xlabel("Age")
plt.legend(["Disease", "No Disease"])
plt.ylabel("Max Heart Rate"); | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Correlation matrix | corr = df.corr()
f, ax = plt.subplots(figsize=(12, 10))
sns.heatmap(corr, annot=True, fmt='.2f', ax=ax);
df.head() | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Modeling | df.head() | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Features / Lable | X = df.drop('target', axis=1)
X.head()
y = df.target
y.head() | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Spliting our dataset with 20% for test | np.random.seed(42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
y_train.head() | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Evaluation metrics Function for geting score (f1 and acc) and ploting the confusion metrix | def getScore(model, X_test, y_test):
y_pred = model.predict(X_test)
print('f1_score')
print(f1_score(y_test,y_pred,average='binary'))
print('accuracy')
acc = accuracy_score(y_test,y_pred, normalize=True)
print(acc)
print('Confusion Matrix :')
plot_confusion_matrix(model, X_test, y_test)
... | f1_score
0.8524590163934426
accuracy
0.8524590163934426
Confusion Matrix :
| MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Classification report | print(classification_report(y_test, clf.predict(X_test))) | precision recall f1-score support
0 0.81 0.90 0.85 29
1 0.90 0.81 0.85 32
accuracy 0.85 61
macro avg 0.85 0.85 0.85 61
weighted avg 0.86 0.85 0.85 ... | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
ROC curve | plot_roc_curve(clf, X_test, y_test); | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Feature importance | clf.coef_
f_dict = dict(zip(X.columns , clf.coef_[0]))
f_data = pd.DataFrame(f_dict, index=[0])
f_data.T.plot.bar(title="Feature Importance", legend=False, figsize=(10,4));
plt.xticks(rotation = 0); | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
from this plot we can see features who have importance or not Cross-validation | cv_precision = np.mean(cross_val_score(MultinomialNB(),
X,
y,
cv=5))
cv_precision | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
GreadSearcheCV | np.random.seed(42)
param_grid = {
'alpha': [0.01, 0.1, 0.5, 1.0, 10.0]
}
grid_search = GridSearchCV(estimator = MultinomialNB(), param_grid = param_grid,
cv = 10, n_jobs = -1, verbose = 2)
grid_search.fit(X_train, y_train)
best_grid = grid_search.best_params_
print('best grid = ', ... | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Comparing results | model_scores = {'MNB': clf_accuracy, 'grid_searche': grid_accuracy}
model_compare = pd.DataFrame(model_scores, index=['accuracy'])
model_compare.T.plot.bar();
plt.xticks(rotation = 0); | _____no_output_____ | MIT | MultinomialNB.ipynb | djendara/heart-disease-classification |
Preparação dos dados | clean_df = pd.DataFrame(columns=df.columns, index=df.index)
residual_df = pd.DataFrame(columns=df.columns, index=df.index)
for col in df.columns:
residual, clean = remove_periodic(df[col].tolist(), df.index, frequency_threshold=0.01e12)
clean_df[col] = clean.tolist()
residual_df[col] = residual.tolist()
tr... | _____no_output_____ | MIT | notebooks/180418 - FCM SpatioTemporal.ipynb | cseveriano/spatio-temporal-forecasting |
Fuzzy C-Means | from pyFTS.partitioners import FCM | _____no_output_____ | MIT | notebooks/180418 - FCM SpatioTemporal.ipynb | cseveriano/spatio-temporal-forecasting |
Adagrad from scratch | from mxnet import ndarray as nd
# Adagrad.
def adagrad(params, sqrs, lr, batch_size):
eps_stable = 1e-7
for param, sqr in zip(params, sqrs):
g = param.grad / batch_size
sqr[:] += nd.square(g)
div = lr * g / nd.sqrt(sqr + eps_stable)
param[:] -= div
import mxnet as mx
from mxnet... | Batch size 10, Learning rate 0.900000, Epoch 1, loss 5.3231e-05
Batch size 10, Learning rate 0.900000, Epoch 2, loss 4.9388e-05
Batch size 10, Learning rate 0.900000, Epoch 3, loss 4.9256e-05
w: [[ 1.99946415 -3.39996123]] b: 4.19967
| Apache-2.0 | chapter06_optimization/adagrad-scratch.ipynb | sgeos/mxnet_the_straight_dope |
Flatten all battles, campains, etc. | def _flatten_battles(battles, root=None):
buttles_to_run = copy(battles)
records = []
for name, data in battles.items():
if 'children' in data:
children = data.pop('children')
records.extend(_flatten_battles(children, root=name))
else:
data['level'] =... | _____no_output_____ | MIT | Chapter11/_json_to_table.ipynb | Drtaylor1701/Learn-Python-by-Building-Data-Science-Applications |
Store as CSV | for front, data in records.items():
data.to_csv(f'./data/{front}.csv', index=None) | _____no_output_____ | MIT | Chapter11/_json_to_table.ipynb | Drtaylor1701/Learn-Python-by-Building-Data-Science-Applications |
Stanza Dependency Parsing Navigation:* [General Info](info)* [Setting up Stanza for training](setup)* [Preparing Dataset for DEPPARSE](prepare)* [Training a Dependency Parser with Stanza](depparse)* [Using Trained Model for Prediction](predict)* [Prediction and Saving to CONLL-U](save) General Info [`Link to Manual`... | #!pip install stanza
# !pip install -U stanza | _____no_output_____ | CC0-1.0 | stanza/stanza_depparse.ipynb | steysie/parse-xplore |
Run in terminal.1. Clone Stanza GitHub repository```$git clone https://github.com/stanfordnlp/stanza```2. Move to cloned git repository & download embeddings ({lang}.vectors.xz format)(run in a screen, takes up several hours, depending on the Internet speed). Make sure the vectors are in `/extern_data/word2vec` folder.... | import stanza
stanza.download('ru') | Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/master/resources_1.0.0.json: 115kB [00:00, 2.47MB/s]
2020-07-24 11:27:31 INFO: Downloading default packages for language: ru (Russian)...
2020-07-24 11:27:32 INFO: File exists: /home/steysie/stanza_resources/ru/default.zip.
2... | CC0-1.0 | stanza/stanza_depparse.ipynb | steysie/parse-xplore |
Preparing Dataset for DEPPARSE | from corpuscula.corpus_utils import syntagrus, download_ud, Conllu
from corpuscula import corpus_utils
import junky
import corpuscula.corpus_utils as cu
import stanza
# cu.set_root_dir('.')
# !pip install -U junky
corpus_utils.download_syntagrus(root_dir=corpus_utils.get_root_dir(), overwrite=True)
junky.clear_tqdm()
... | Load corpus
| CC0-1.0 | stanza/stanza_depparse.ipynb | steysie/parse-xplore |
Training a Dependency Parser with Stanza **`STEP 1`**`Input files for DEPPARSE model training should be placed here:` **`{UDBASE}/{corpus}/{corpus_short}-ud-{train,dev,test}.conllu`**, where * **`{UDBASE}`** is `./stanza/udbase/` (specified in `config.sh`), * **`{corpus}`** is full corpus name (e.g. `UD_Russian-SynTag... | nlp = stanza.Pipeline('ru',
processors='tokenize,pos,lemma,ner,depparse',
depparse_model_path='stanza/saved_models/depparse/ru_syntagrus_parser.pt',
tokenize_pretokenized=True)
doc = nlp(' '.join(test[0]))
print(*[f'id: {word.id}\tword: {word... | _____no_output_____ | CC0-1.0 | stanza/stanza_depparse.ipynb | steysie/parse-xplore |
Prediction and Saving Results to CONLL-U | junky.clear_tqdm()
Conllu.save(stanza_parse(test), 'stanza_syntagrus.conllu',
fix=True, log_file=None) | 2020-07-28 13:24:45 INFO: Loading these models for language: ru (Russian):
=======================================
| Processor | Package |
---------------------------------------
| tokenize | syntagrus |
| pos | syntagrus |
| lemma | syntagrus |
| dep... | CC0-1.0 | stanza/stanza_depparse.ipynb | steysie/parse-xplore |
Inference on Test Corpus | # !pip install mordl
from mordl import conll18_ud_eval
gold_file = 'corpus/_UD/UD_Russian-SynTagRus/ru_syntagrus-ud-test.conllu'
system_file = 'stanza_syntagrus.conllu'
conll18_ud_eval(gold_file, system_file, verbose=True, counts=False) | 0%| | 0/6491 [34:50<?, ?it/s]
| CC0-1.0 | stanza/stanza_depparse.ipynb | steysie/parse-xplore |
ISFOG 2020 - Pile driving prediction eventData science techniques are rapidly transforming businesses in a broad range of sectors. While marketing and social applications have received most attention to date, geotechnical engineering can also benefit from data science tools that are now readily available. In the conte... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
%matplotlib inline | _____no_output_____ | Apache-2.0 | analysis/isfog-2020-linear-model-demo.ipynb | alexandershires/offshore-geo |
2. Pile driving dataThe dataset is kindly provided by [Cathie Group](http://www.cathiegroup.com). 2.1. Importing dataThe first step in any data science exercise is to get familiar with the data. The data is provided in a csv file (```training_data.csv```). We can import the data with Pandas and display the first five ... | data = pd.read_csv("/kaggle/input/training_data.csv") # Store the contents of the csv file in the variable 'data'
data.head() | _____no_output_____ | Apache-2.0 | analysis/isfog-2020-linear-model-demo.ipynb | alexandershires/offshore-geo |
The data has 12 columns, containing PCPT data ($ q_c $, $ f_s $ and $ u_2 $), recorded hammer data (blowcount, normalised hammer energy, normalised ENTHRU and total number of blows), pile data (diameter, bottom wall thickness and pile final penetration). A unique ID identifies the location and $ z $ defines the depth b... | data.describe() | _____no_output_____ | Apache-2.0 | analysis/isfog-2020-linear-model-demo.ipynb | alexandershires/offshore-geo |
2.3. PlottingWe can plot the cone tip resistance, blowcount and normalised ENTHRU energy for all locations to show how the data varies with depth. We can generate this plot using the ```Matplotlib``` package. | fig, ((ax1, ax2, ax3)) = plt.subplots(1, 3, sharey=True, figsize=(16,9))
ax1.scatter(data["qc [MPa]"], data["z [m]"], s=5) # Create the cone tip resistance vs depth plot
ax2.scatter(data["Blowcount [Blows/m]"], data["z [m]"], s=5) # Create the Blowcount vs depth plot
ax3.scatter(data["Normalised ENTRHU [-]"], data[... | _____no_output_____ | Apache-2.0 | analysis/isfog-2020-linear-model-demo.ipynb | alexandershires/offshore-geo |
The cone resistance data shows that the site mainly consists of sand of varying relative density. In certain profiles, clay is present below 10m. There are also locations with very high cone resistance (>70MPa).The blowcount profile shows that blowcount is relatively well clustered around a generally increasing trend w... | # Select the data where the column 'Location ID' is equal to the location name
location_data = data[data["Location ID"] == "EK"] | _____no_output_____ | Apache-2.0 | analysis/isfog-2020-linear-model-demo.ipynb | alexandershires/offshore-geo |
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