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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データセットの用意**MNISTデータセットから「０」と「１」のみを抽出します** | #データの前処理を行うクラスインスタンス
transform = transforms.Compose(
[transforms.Resize((16, 16)),
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])
batch_size = 100
#使用するtrainデータセット
trainset = torchvision.datasets.MNIST(root='./data',
train=True,
... | 10000
1000
| MIT | text/Chapter9.ipynb | Selubi/tutorial_python |
モデルの定義 | import torch.nn.functional as F
#モデルの定義
class NeuralNet(torch.nn.Module):
def __init__(self, n_input=256, n_hidden=16, n_output=8):
super(NeuralNet, self).__init__()
self.n_input = n_input
#一層目と二層目の重み行列の定義
self.l1 = torch.nn.Linear(n_input, n_hidden, bias = True)
se... | _____no_output_____ | MIT | text/Chapter9.ipynb | Selubi/tutorial_python |
一度予測をさせてみます．\前回作成したモデルは2~9までの数字しか分類できないので結果はトンチキなものになります | #モデルの予想を可視化する関数の作成
def prediction(model, num=10, c=2):
with torch.no_grad():
img, t = testloader.__iter__().next()
t_pred = model(img)
fig = plt.figure(figsize=(12,4))
ax = []
for i in range(num):
print(f'true: {t[i]}, predict: {np.argmax(t_pred[i])+c}')
... | true: 1, predict: 8
true: 1, predict: 2
true: 0, predict: 5
true: 1, predict: 8
true: 0, predict: 5
true: 0, predict: 2
true: 0, predict: 5
true: 1, predict: 3
true: 0, predict: 5
true: 1, predict: 7
| MIT | text/Chapter9.ipynb | Selubi/tutorial_python |
転移学習の用意をします． | for param in model.parameters():
param.requires_grad = False#モデルにある全てのパラメータを固定値に変換する
model.l3 = torch.nn.Linear(model.l3.in_features, 2)#モデルの最終層のパラメータを2クラス分類ように書き換えた上で学習パラメータに設定します
model.l3.requires_grad = True#デフォルトでTrueなので本来は書く必要ないですが明示的に
#loss関数の設定
criterion = torch.nn.CrossEntropyLoss()
#最適化手法の設定
optimiz... | true: 1, predict: 0
true: 1, predict: 1
true: 0, predict: 0
true: 1, predict: 1
true: 0, predict: 0
true: 0, predict: 1
true: 0, predict: 0
true: 1, predict: 1
true: 0, predict: 0
true: 1, predict: 1
| MIT | text/Chapter9.ipynb | Selubi/tutorial_python |
ある程度の分類ができるようになっていると思います．\うまく分類できているのが確認できたら，モデルのパラメータの代入部分```pythonmodel.load_state_dict(torch.load("params/model_state_dict.pth"), strict=False)```などをコメントアウトしてもう一度学習させてみましょう．おそらく分類精度が悪くなってしまうのが確認できると思います | torch.save(model.state_dict(), "params/01_model_state_dict.pth") | _____no_output_____ | MIT | text/Chapter9.ipynb | Selubi/tutorial_python |
k-Nearest Neighbor (kNN) exercise*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*The k... | %matplotlib
# Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figs... | _____no_output_____ | MIT | assign/assignment1/knn.ipynb | zhmz90/CS231N |
We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: 1. First we must compute the distances between all test examples and all train examples. 2. Given these distances, for each test example we find the k nearest examples and have them vote for t... | # Open cs231n/classifiers/k_nearest_neighbor.py and implement
# compute_distances_two_loops.
# Test your implementation:
dists = classifier.compute_distances_two_loops(X_test)
# We can visualize the distance matrix: each row is a single test example and
# its distances to training examples
plt.imshow(dists, interpolat... | _____no_output_____ | MIT | assign/assignment1/knn.ipynb | zhmz90/CS231N |
**Inline Question 1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)- What in the data is the cause behind the distinctly bright rows?- What causes the ... | # Now implement the function predict_labels and run the code below:
# We use k = 1 (which is Nearest Neighbor).
y_test_pred = classifier.predict_labels(dists, k=1)
# Compute and print the fraction of correctly predicted examples
num_correct = np.sum(y_test_pred == y_test)
accuracy = float(num_correct) / num_test
print... | Got 137 / 500 correct => accuracy: 0.274000
| MIT | assign/assignment1/knn.ipynb | zhmz90/CS231N |
You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`: | y_test_pred = classifier.predict_labels(dists, k=5)
num_correct = np.sum(y_test_pred == y_test)
accuracy = float(num_correct) / num_test
print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy) | Got 142 / 500 correct => accuracy: 0.284000
| MIT | assign/assignment1/knn.ipynb | zhmz90/CS231N |
You should expect to see a slightly better performance than with `k = 1`. | # Now lets speed up distance matrix computation by using partial vectorization
# with one loop. Implement the function compute_distances_one_loop and run the
# code below:
dists_one = classifier.compute_distances_one_loop(X_test)
# To ensure that our vectorized implementation is correct, we make sure that it
# agrees ... | One loop version took 48.089830 seconds
| MIT | assign/assignment1/knn.ipynb | zhmz90/CS231N |
Cross-validationWe have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation. | num_folds = 5
k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]
X_train_folds = []
y_train_folds = []
################################################################################
# TODO: #
# Split up the training data into folds. After splittin... | Got 141 / 500 correct => accuracy: 0.282000
| MIT | assign/assignment1/knn.ipynb | zhmz90/CS231N |
Дистилляция BERTДообученная модель BERT показывает очень хорошее качество при решении множества NLP-задач. Однако, её не всегда можно применить на практике из-за того, что модель очень большая и работает дастаточно медленно. В связи с этим было придумано несколько способов обойти это ограничение.Один из способов - `k... | pip install transformers catboost
import os
import random
import numpy as np
import pandas as pd
import torch
from transformers import AutoConfig, AutoModelForSequenceClassification
from transformers import AutoTokenizer
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from catboost import Po... | cuda
Tesla P100-PCIE-16GB
| MIT | BERT_distyll.ipynb | blanchefort/text_mining |
Загрузка токенизатора, модели, конфигурации | # config
config = AutoConfig.from_pretrained('/content/drive/My Drive/colab_data/leroymerlin/model/BERT_model')
# tokenizer
tokenizer = AutoTokenizer.from_pretrained('/content/drive/My Drive/colab_data/leroymerlin/model/BERT_model', pad_to_max_length=True)
# model
model = AutoModelForSequenceClassification.from_pretrai... | _____no_output_____ | MIT | BERT_distyll.ipynb | blanchefort/text_mining |
Подготовка данных | category_index = {'Водоснабжение': 8,
'Декор': 12,
'Инструменты': 4,
'Краски': 11,
'Кухни': 15,
'Напольные покрытия': 5,
'Окна и двери': 2,
'Освещение': 13,
'Плитка': 6,
'Сад': 9,
'Сантехника': 7,
'Скобяные изделия': 10,
'Столярные изделия': 1,
'Стройматериалы': 0,
'Хранение': 14,
'Электротовары': 3}
cat... | _____no_output_____ | MIT | BERT_distyll.ipynb | blanchefort/text_mining |
Получение логитов BERT | train_logits = []
with torch.no_grad():
model.to(device)
for batch in tqdm(dataloader):
batch = batch.to(device)
outputs = model(batch)
logits = outputs[0].detach().cpu().numpy()
train_logits.extend(logits)
#train_logits = np.vstack(train_logits) | _____no_output_____ | MIT | BERT_distyll.ipynb | blanchefort/text_mining |
Обучение ученикаТеперь возьмём мультирегрессионную модель от CatBoost и передадим ей все полученные логиты. | data_pool = Pool(tokens, train_logits)
distilled_model = CatBoostRegressor(iterations=2000,
depth=4,
learning_rate=.1,
loss_function='MultiRMSE',
verbose=200)
distilled_model.fit(data_pool) | 0: learn: 11.6947874 total: 275ms remaining: 9m 9s
200: learn: 9.0435970 total: 47s remaining: 7m
400: learn: 8.2920608 total: 1m 32s remaining: 6m 10s
600: learn: 7.7736947 total: 2m 18s remaining: 5m 22s
800: learn: 7.3674586 total: 3m 4s remaining: 4m 36s
1000: learn: 7.0166625 total: 3m 51s remaining: 3m 51s
1200: ... | MIT | BERT_distyll.ipynb | blanchefort/text_mining |
Сравнение качества моделей | category_index_inverted = dict(map(reversed, category_index.items())) | _____no_output_____ | MIT | BERT_distyll.ipynb | blanchefort/text_mining |
Метрики Берта: | print(classification_report(labels, np.argmax(train_logits, axis=1), target_names=category_index_inverted.values())) | precision recall f1-score support
Водоснабжение 0.94 0.88 0.91 13377
Декор 1.00 0.40 0.57 2716
Инструменты 1.00 0.40 0.58 540
Краски 0.97 0.81 0.88 20397
Кухни ... | MIT | BERT_distyll.ipynb | blanchefort/text_mining |
Метрики модели-ученика: | tokens_pool = Pool(tokens)
distilled_predicted_logits = distilled_model.predict(tokens_pool, prediction_type='RawFormulaVal') # Probability
print(classification_report(labels, np.argmax(distilled_predicted_logits, axis=1), target_names=category_index_inverted.values())) | precision recall f1-score support
Водоснабжение 0.90 0.53 0.67 13377
Декор 0.99 0.30 0.46 2716
Инструменты 0.00 0.00 0.00 540
Краски 0.97 0.61 0.75 20397
Кухни ... | MIT | BERT_distyll.ipynb | blanchefort/text_mining |
TensorBayes Adaptation of `BayesC.cpp` Imports | import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np
tfd = tfp.distributions | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
File inputTo do | # Get the numbers of columns in the csv:
# File I/O here
filenames = ""
csv_in = open(filenames, "r") # open the csv
ncol = len(csv_in.readline().split(",")) # read the first line and count the # of columns
csv_in.close() # close the csv
print("Nu... | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
ReproducibilitySeed setting for reproducable research. | # To do: get a numpy seed or look at how TF implements rng.
# each distributions.sample() seen below can be seedeed.
# ex. dist.sample(seed=32): return a sample of shape=() (scalar).
# Set graph-level seed
tf.set_random_seed(1234) | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
Distributions functions- Random Uniform: return a sample from a uniform distribution of limits parameter `lower ` and `higher`. - Random Normal: return a sample from a normal distribution of parameter `mean` and `standard deviation`. - Random Beta: return a random quantile of a beta distribution of par... | # Note: written as a translation of BayesC.cpp
# the function definitions might not be needeed,
# and the declarations of the distributions could be enough
def runif(lower, higher):
dist = tfd.Uniform(lower, higher)
return dist.sample()
def rnorm(mean, sd):
dist = tfd.Normal(loc= mean, scale= sd)
retu... | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
Sampling functions- Sampling of the mean - Sampling of the variance of beta - Sampling of the error variance of Y - Sample of the mixture weight | # sample mean
def sample_mu(N, Esigma2, Y, X, beta): #as in BayesC, with the N parameter
mean = tf.reduce_sum(tf.subtract(Y, tf.matmul(X, beta)))/N
sd = tf.sqrt(Esigma2/N)
mu = rnorm(mean, sd)
return mu
# sample variance of beta
def sample_psi2_chisq( beta, NZ, v0B, s0B):
df=v0B+NZ
scale=(tf.nn... | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
Simulate data | def build_toy_dataset(N, beta, sigmaY_true=1):
features = len(beta)
x = np.random.randn(N, features)
y = np.dot(x, beta) + np.random.normal(0, sigmaY_true, size=N)
return x, y
N = 40 # number of data points
M = 10 # number of features
beta_true = np.random.randn(M)
x, y = build_toy_dataset(N, b... | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
Parameters setup | # Distinction between constant and variables
# Variables: values might change between evaluation of the graph
# (if something changes within the graph, it should be a variable)
Emu = tf.Variable(0., trainable=False)
vEmu = tf.ones([N,1])
Ebeta = tf.zeros([M,1])
ny = tf.zeros(M)
Ew = tf.constant(0.)
epsilon = Y - tf.ma... | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
Tensorboard graph | writer = tf.summary.FileWriter('.')
writer.add_graph(tf.get_default_graph()) | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
Gibbs sampling | # Open session
sess = tf.Session()
# Initialize variables
init = tf.global_variables_initializer()
sess.run(init)
num_iter = 50
print(sess.run(tf.report_uninitialized_variables()))
#debug for just 1 marker 0
epsilon = tf.add(epsilon, X[:,0]*Ebeta[0])
Cj=tf.nn.l2_loss(X[:,0])*2+Esigma2/Epsi2 #adjusted variance
rj= tf.m... | _____no_output_____ | MIT | notebooks/TensorBayes.ipynb | jklopf/tensorbayes |
track features | del_water_tracks[:5]
def tracks_toDF(tracks):
records = []
for track in tracks:
audio_features = sp.audio_features(track['id'])[0]
audio_features['artists'] = track['artists']
audio_features['name'] = track['name']
records.append(audio_features)
df = pd.DataFrame.from_record... | _____no_output_____ | MIT | test.ipynb | schlinkertc/Spotify |
Visualize | import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
sns.set(color_codes=True)
sns.set(rc={'figure.figsize':(20,18)})
nick_df = tracks_toDF(nick_tracks)
nick_data = nick_df.groupby('name').mean()
def track_scatterPlot(data,x... | _____no_output_____ | MIT | test.ipynb | schlinkertc/Spotify |
by playlist | sam_playlists = sp.user_playlists(1250134147)
sam_playlist_ids = [
{'playlist_id':x['id'],'playlist_name':x['name']} for x in sam_playlists['items']]
sam_playlist_ids
records = []
for playlist in sam_playlist_ids:
playlist_items = sp.playlist_tracks(playlist['playlist_id'])['items']
track_ids = [item['track... | _____no_output_____ | MIT | test.ipynb | schlinkertc/Spotify |
Songs I've played on | my_tracks=[
{
'name':item['track']['name'],
'id':item['track']['id'],
'artists':[x['name'] for x in item['track']['artists']]
} for
item in sp.playlist_tracks("spotify:playlist:4XeFzR948Yyk1X4SsXXogr")['items']
]
my_df = tracks_toDF(my_tracks)
track_scatterPlot(my_df,'valence','ener... | _____no_output_____ | MIT | test.ipynb | schlinkertc/Spotify |
Tutorial 3In tutorial 1 and 2, we have seen how to use great expectation as a project framework to validate data. If you don't want to useall the features that it provides you. you can just use the simple validation method directly on a dataframe | import great_expectations as ge
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
file_path="../data/adult_with_duplicates.csv"
df = pd.read_csv(file_path)
# convert pandas dataframe to ge dataframe
df = ge.dataset.PandasDataset(df)
print(df.columns)
df.head() | _____no_output_____ | Apache-2.0 | 02.great_expectations_validation/3.Valid_data_via_function.ipynb | pengfei99/DataQualityAndValidation |
Apply validation method directly on the dataframeBelow method checks if the dataframe has the expected column names. It's equivalent to the yaml config```yaml"expectations": [ { "expectation_type": "expect_table_columns_to_match_ordered_list", "kwargs": { "column_list": [ "age", "... | column_list= [
"age",
"workclass",
"fnlwgt",
"education",
"education-num",
"marital-status",
"occupation",
"relationship",
"race",
"sex",
"capital-gain",
"capital-loss",
"hours-per-week",
... | _____no_output_____ | Apache-2.0 | 02.great_expectations_validation/3.Valid_data_via_function.ipynb | pengfei99/DataQualityAndValidation |
Below method checks if the age value is between 0 and 120. It's equivalent to the yaml config```yaml { "expectation_type": "expect_column_values_to_be_between", "kwargs": { "column": "age", "max_value": 120.0, "min_value": 0.0 }, "meta": {} }``` | # ge dataframe provides access to all validation method
df.expect_column_values_to_be_between(column='age', min_value=0, max_value=120)
df.expect_column_values_to_not_be_null("age")
values= ("Private", "Self-emp-not-inc", "Self-emp-inc", "Federal-gov", "Local-gov", "State-gov", "Without-pay", "Never-worked")
df.expect... | _____no_output_____ | Apache-2.0 | 02.great_expectations_validation/3.Valid_data_via_function.ipynb | pengfei99/DataQualityAndValidation |
Expand Hillsborough data set with additional ACS variables Header information*DataDive goal targeted:* "Expand the list of ACS variables available for analysis by joining the processed dataset with the full list of data profile variables."*Contact info*: Josh Hirner, jhirner@gmail.com | import pandas as pd | _____no_output_____ | MIT | scripts/_jhirner/hillsborough_expand_acs_variables.ipynb | ahopejasen/datakind-test |
Import the required data sets:(1) Procecessed housing insecurity data as `hb_proc`, (2) the ACS demographic dataset as `hb_acs`, and (3) the ACS data dictionary for interpeting `DPxx_xxxx` codes as `acs_dict`. | hb_proc = pd.read_csv("../../data/processed/hillsborough_fl_processed_2017_to_2019_20210225.csv")
hb_proc.head()
hb_acs = pd.read_csv("../../data/acs/hillsborough_acs5-2018_census.csv")
hb_acs.head()
acs_dict = pd.read_csv("../../data/acs/data_dictionary.csv")
acs_dict.head() | _____no_output_____ | MIT | scripts/_jhirner/hillsborough_expand_acs_variables.ipynb | ahopejasen/datakind-test |
Expand the processed data set.Join `hb_proc` (processed Hillsborough housing insecurity) and `hb_acs` (Hillsborough ACS demographics) datasets on the GEO ID columns to generate the expanded Hillsborough dataset, `hb_expand`. | hb_expand = pd.merge(hb_proc, hb_acs, left_on = "census_tract_GEOID", right_on = "GEOID", how = "inner")
hb_expand = hb_expand.drop(["GEOID", "index"], axis = 1)
hb_expand.head() | _____no_output_____ | MIT | scripts/_jhirner/hillsborough_expand_acs_variables.ipynb | ahopejasen/datakind-test |
Quick evaluation for new correlationsLet's see if anything interesting popped up in this merge. For illustrative purposes only, we'll restrict this correlation to the `avg-housing-loss-rate` column from the original processed Hillsborough data. | hb_corr = hb_expand.corr(method = "spearman")
# Examine correlation coefficients only for avg-housing-loss-rate,
# and only with newly merged columns (i.e.: not present in the original processed data set)
hb_housing_loss_corr = hb_corr["avg-housing-loss-rate"].dropna().drop(hb_proc.columns, axis = 0, errors = "ignore"... | _____no_output_____ | MIT | scripts/_jhirner/hillsborough_expand_acs_variables.ipynb | ahopejasen/datakind-test |
At a very cursory glance, it appears as though the expanded ACS variables offer both strong positive and strong negative correlations to housing insecurity. Export the expanded data | hb_expand.to_csv("../../data/processed/hillsborough_fl_processed_expanded_ACS_2017_to_2019_20210225.csv") | _____no_output_____ | MIT | scripts/_jhirner/hillsborough_expand_acs_variables.ipynb | ahopejasen/datakind-test |
Table of Contents1 Clustering based1.1 modeling1.2 prediction1.3 evaluation | from sklearn.metrics import pairwise_distances
from sklearn import metrics
from sklearn import mixture
from sklearn.cluster import KMeans
from nltk.cluster import KMeansClusterer, cosine_distance
import pandas as pd
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.pipeline import Pipeline... |
may use cols:
['global_index', 'doc_path', 'label', 'reply', 'reference_one', 'reference_two', 'Subject', 'From', 'Lines', 'Organization', 'contained_emails', 'long_string', 'text', 'error_message']
| MIT | code/history_backup/clustering_based_models_v1-reply_reference-200_dim.ipynb | InscribeDeeper/Text-Classification |
Clustering based- Steps: 1. Transform into TF-IDF matrix 2. Dimension reduction into 200 3. Clustering in cosine similarity space (since it is word) 4. Assign labels with majority vote based on training set labels 5. Prediction 1. Transform test set into TF-IDF matrix 2. Dimension reductio... | train_text = train['reply'] + ' ' + train['reference_one']
train_label = train['label']
test_text = test['reply'] + ' ' + test['reference_one']
test_label = test['label']
from sklearn.decomposition import TruncatedSVD
def tfidf_vectorizer(train_text, test_text, min_df=3):
tfidf_vect = TfidfVectorizer(stop_words=... | C:\Users\Administrator\Anaconda3\envs\py810\lib\site-packages\nltk\cluster\util.py:131: RuntimeWarning: invalid value encountered in double_scalars
return 1 - (numpy.dot(u, v) / (sqrt(numpy.dot(u, u)) * sqrt(numpy.dot(v, v))))
| MIT | code/history_backup/clustering_based_models_v1-reply_reference-200_dim.ipynb | InscribeDeeper/Text-Classification |
prediction | pred = pred_clustering_model(dtm_test, clusterer, clusters_to_labels) | _____no_output_____ | MIT | code/history_backup/clustering_based_models_v1-reply_reference-200_dim.ipynb | InscribeDeeper/Text-Classification |
evaluation | from sklearn import preprocessing
# le = preprocessing.LabelEncoder()
# encoded_test_label = le.fit_transform(test_label)
# print(metrics.classification_report(y_true = encoded_test_label, y_pred=pred, target_names=le.classes_))
print(metrics.classification_report(y_true = test_label, y_pred=pred)) | _____no_output_____ | MIT | code/history_backup/clustering_based_models_v1-reply_reference-200_dim.ipynb | InscribeDeeper/Text-Classification |
Artificial Neural Networks Geo-Demographic Segmentation | import tensorflow as tf
import numpy as np
import pandas as pd
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config) | _____no_output_____ | MIT | Machine Learning Projects/Useful_Code_Examples/Deep_Learning/ANN-GeoDemographicSegmentation/ANN-GridSearch.ipynb | samlaubscher/HacktoberFest2020-Contributions |
Part 1 - Data Preprocessing Data Loading | PATH = "../../../../Deep_Learning/ANN/Python/Churn_Modelling.csv"
dataset = pd.read_csv(PATH)
dataset.head()
X = dataset.iloc[:, 3:-1].values
y = dataset.iloc[:, -1].values | _____no_output_____ | MIT | Machine Learning Projects/Useful_Code_Examples/Deep_Learning/ANN-GeoDemographicSegmentation/ANN-GridSearch.ipynb | samlaubscher/HacktoberFest2020-Contributions |
Encoding the Categorical Variables | from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
X[:, 2] = le.fit_transform(X[:, 2])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder',
OneHotEncoder(), [1])],
... | _____no_output_____ | MIT | Machine Learning Projects/Useful_Code_Examples/Deep_Learning/ANN-GeoDemographicSegmentation/ANN-GridSearch.ipynb | samlaubscher/HacktoberFest2020-Contributions |
Train Test Split | from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.20,
random_state=42) | _____no_output_____ | MIT | Machine Learning Projects/Useful_Code_Examples/Deep_Learning/ANN-GeoDemographicSegmentation/ANN-GridSearch.ipynb | samlaubscher/HacktoberFest2020-Contributions |
Feature Scaling | from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test) | _____no_output_____ | MIT | Machine Learning Projects/Useful_Code_Examples/Deep_Learning/ANN-GeoDemographicSegmentation/ANN-GridSearch.ipynb | samlaubscher/HacktoberFest2020-Contributions |
Grid Search | from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
def build_classifer(optimizer='adam'):
tf.random.set_seed(42)
classifier = Sequential()
classi... | _____no_output_____ | MIT | Machine Learning Projects/Useful_Code_Examples/Deep_Learning/ANN-GeoDemographicSegmentation/ANN-GridSearch.ipynb | samlaubscher/HacktoberFest2020-Contributions |
LSTM ModelTrain a LSTM (long short term memory) model to forecast time series sequence of stock closing price Use 5 time steps to forecast 1 forward time step Imports | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import tensorflow as tf
| _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Upload closing price data | df = pd.read_csv('fb_rsi.csv')
df['date'] = pd.to_datetime(df['date']) | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Convert pandas object to numpy array | close = np.array(df['close'])
date = np.array(df['date'])
print(len(close)) | 160679
| Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
There are 160,679 time steps, split 100,000 for training and the rest for testing | split = 100000
date_train = date[:split] #Training split
x_train = close[:split]
date_val = date[split:] #Testing split
x_val = close[split:]
#Variables for the windowing function below
window_size = 5 #Number of timestep
batch_size = 250 #Number of sequence to be loaded into the model, depends to GPU memory
shuffle_b... | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Window function to split the sequence into features and labels Sequence is split into windows of 5 time steps as the feature and the next time step as the label | def windowed_dataset(series, window_size, batch_size, shuffle_buffer):
series = tf.expand_dims(series, axis=-1)
ds = tf.data.Dataset.from_tensor_slices(series)
ds = ds.window(window_size + 1, shift=1, drop_remainder=True)
ds = ds.flat_map(lambda w: w.batch(window_size + 1))
ds = ds.shuffle(shuffle_b... | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
LSTM ModelCombine convolutional layers with LSTM layers for the complete model | train_set = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv1D(filters=60, kernel_size=5,
strides=1, padding='causal',
activation='relu', input_shape=[None, 1]), #1D convolution ... | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Train model for 600 epochs Average 7 seconds per epoch, training time took 1 hour 10 minutes | history = model.fit(train_set, epochs=600)
Plot MAE and loss against epochs
mae=history.history['mae']
loss=history.history['loss']
epochs=range(len(loss))
plt.plot(epochs, mae, 'r')
plt.plot(epochs, loss, 'b')
plt.title('MAE and Loss')
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend(["MAE", "Loss"])
plt.show... | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Zooming into the last 100 epochs, although there are fluctuations, MAE and losses are still decreasing Model can be trained longer for higher accuracy | mae=history.history['mae']
loss=history.history['loss']
epochs=range(len(loss))
plt.plot(epochs[-100:], mae[-100:], 'r')
plt.plot(epochs[-100:], loss[-100:], 'b')
plt.title('MAE and Loss')
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend(["MAE", "Loss"])
plt.show() | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Saving the model | model.save('lstm.h5') | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Function to forecast test data Similar to training, forecasting uses windows of 5 time steps to forecast forward time step | def model_forecast(model, series, window_size):
ds = tf.data.Dataset.from_tensor_slices(series)
ds = ds.window(window_size, shift=1, drop_remainder=True)
ds = ds.flat_map(lambda w: w.batch(window_size))
ds = ds.batch(250).prefetch(1)
forecast = model.predict(ds)
return forecast | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Forecast testing data | forecast = model_forecast(model, close[..., np.newaxis], window_size)
forecast = forecast[split - window_size:-1, -1, 0] | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Visualising forecasted time seriesForecasted series looks similar to the actual stock, however the model was unable to match the peaks of the closing prices | locator = mdates.MonthLocator()
fmt = mdates.DateFormatter('%b %y')
plt.plot(date_val, x_val, alpha=0.5)
plt.plot(date_val, forecast)
x = plt.gca()
x.xaxis.set_major_locator(locator)
x.xaxis.set_major_formatter(fmt)
plt.title('Actual vs Forecast')
plt.legend(['Actual', 'Forecast'])
plt.xticks(rotation=45)
plt.show() | _____no_output_____ | Apache-2.0 | lstm_fb.ipynb | ctxj/Financial-Time-Series |
Development of EDT residual | import itertools
def edt(self, coords):
arrivals = self.arrivals.set_index("handle")
pairs = list(itertools.product(arrivals.index, arrivals.index))
r = [
(arrivals.loc[handle1, "time"] - arrivals.loc[handle2, "time"] )
- (self._tt[handle1].value(coords[:3], null=np.inf) - self._tt[handle2]... | _____no_output_____ | MIT | pykonal_eq/quake_de.ipynb | malcolmw/PyKonalEQ |
Precision-RecallExample of Precision-Recall metric to evaluate classifier output quality.Precision-Recall is a useful measure of success of prediction when theclasses are very imbalanced. In information retrieval, precision is ameasure of result relevancy, while recall is a measure of how many trulyrelevant results ar... | from __future__ import print_function | _____no_output_____ | MIT | scikit-learn-official-examples/model_selection/plot_precision_recall.ipynb | gopala-kr/ds-notebooks |
In binary classification settings--------------------------------------------------------Create simple data..................Try to differentiate the two first classes of the iris data | from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
import numpy as np
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Add noisy features
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]... | _____no_output_____ | MIT | scikit-learn-official-examples/model_selection/plot_precision_recall.ipynb | gopala-kr/ds-notebooks |
Compute the average precision score................................... | from sklearn.metrics import average_precision_score
average_precision = average_precision_score(y_test, y_score)
print('Average precision-recall score: {0:0.2f}'.format(
average_precision)) | _____no_output_____ | MIT | scikit-learn-official-examples/model_selection/plot_precision_recall.ipynb | gopala-kr/ds-notebooks |
Plot the Precision-Recall curve................................ | from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt
precision, recall, _ = precision_recall_curve(y_test, y_score)
plt.step(recall, precision, color='b', alpha=0.2,
where='post')
plt.fill_between(recall, precision, step='post', alpha=0.2,
color='b')
plt.xlabel... | _____no_output_____ | MIT | scikit-learn-official-examples/model_selection/plot_precision_recall.ipynb | gopala-kr/ds-notebooks |
In multi-label settings------------------------Create multi-label data, fit, and predict...........................................We create a multi-label dataset, to illustrate the precision-recall inmulti-label settings | from sklearn.preprocessing import label_binarize
# Use label_binarize to be multi-label like settings
Y = label_binarize(y, classes=[0, 1, 2])
n_classes = Y.shape[1]
# Split into training and test
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5,
... | _____no_output_____ | MIT | scikit-learn-official-examples/model_selection/plot_precision_recall.ipynb | gopala-kr/ds-notebooks |
The average precision score in multi-label settings.................................................... | from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
# For each class
precision = dict()
recall = dict()
average_precision = dict()
for i in range(n_classes):
precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i],
... | _____no_output_____ | MIT | scikit-learn-official-examples/model_selection/plot_precision_recall.ipynb | gopala-kr/ds-notebooks |
Plot the micro-averaged Precision-Recall curve............................................... | plt.figure()
plt.step(recall['micro'], precision['micro'], color='b', alpha=0.2,
where='post')
plt.fill_between(recall["micro"], precision["micro"], step='post', alpha=0.2,
color='b')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title(
'Avera... | _____no_output_____ | MIT | scikit-learn-official-examples/model_selection/plot_precision_recall.ipynb | gopala-kr/ds-notebooks |
Plot Precision-Recall curve for each class and iso-f1 curves............................................................. | from itertools import cycle
# setup plot details
colors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal'])
plt.figure(figsize=(7, 8))
f_scores = np.linspace(0.2, 0.8, num=4)
lines = []
labels = []
for f_score in f_scores:
x = np.linspace(0.01, 1)
y = f_score * x / (2 * x - f_score)
l, =... | _____no_output_____ | MIT | scikit-learn-official-examples/model_selection/plot_precision_recall.ipynb | gopala-kr/ds-notebooks |
Regression test suite: Test of basic SSP GCE features Test of SSP with artificial yields,pure h1 yields, provided in NuGrid tables (no PopIII tests here). Focus are basic GCE features.You can find the documentation here.Before starting the test make sure that use the standard yield input files. Outline: $\odot$ Evolu... | #from imp import *
#s=load_source('sygma','/home/nugrid/nugrid/SYGMA/SYGMA_online/SYGMA_dev/sygma.py')
#%pylab nbagg
import sys
import sygma as s
print s.__file__
reload(s)
s.__file__
#import matplotlib
#matplotlib.use('nbagg')
import matplotlib.pyplot as plt
#matplotlib.use('nbagg')
import numpy as np
from scipy.integ... | /Users/christian/Research/NuGRid/NuPyCEE/sygma.py
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
IMF notes: The IMF allows to calculate the number of stars $N_{12}$ in the mass interval [m1,m2] with(I) $N_{12}$ = k_N $\int _{m1}^{m2} m^{-2.35} dm$ Where k_N is the normalization constant. It can be derived from the total amount of mass of the system $M_{tot}$since the total mass $M_{12}$ in the mass interval above... | k_N=1e11*0.35/ (1**-0.35 - 30**-0.35) #(I) | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
The total number of stars $N_{tot}$ is then: | N_tot=k_N/1.35 * (1**-1.35 - 30**-1.35) #(II)
print N_tot | 36877281297.2
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
With a yield ejected of $0.1 Msun$, the total amount ejected is: | Yield_tot=0.1*N_tot
print Yield_tot/1e11 | 0.0368772812972
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
compared to the simulation: | import sygma as s
reload(s)
s1=s.sygma(iolevel=0,mgal=1e11,dt=1e7,imf_type='salpeter',imf_bdys=[1,30],iniZ=0.02,hardsetZ=0.0001,
table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=False, sn1a_table='yield_tables/sn1a_h1.txt',
iniabu_table='yield_tables/iniabu/iniab_h1.ppn',pop3_table='yiel... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Compare both results: | print Yield_tot_sim
print Yield_tot
print 'ratio should be 1 : ',Yield_tot_sim/Yield_tot | 3687728129.72
3687728129.72
ratio should be 1 : 1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Test of distinguishing between massive and AGB sources: Boundaries between AGB and massive for Z=0 (1e-4) at 8 (transitionmass parameter) | Yield_agb= ( k_N/1.35 * (1**-1.35 - 8.**-1.35) ) * 0.1
Yield_massive= ( k_N/1.35 * (8.**-1.35 - 30**-1.35) ) * 0.1
print 'Should be 1:',Yield_agb/s1.history.ism_iso_yield_agb[-1][0]
print 'Should be 1:',Yield_massive/s1.history.ism_iso_yield_massive[-1][0]
print 'Test total number of SNII agree with massive star yield... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Calculating yield ejection over time For plotting, take the lifetimes/masses from the yield grid:$Ini Mass & Age [yrs]1Msun = 5.67e91.65 = 1.211e92 = 6.972e83 = 2.471e84 = 1.347e85 = 8.123e76 = 5.642e77 = 4.217e712 = 1.892e715 = 1.381e720 = 9.895e625 = 7.902e6$ | s1=s.sygma(iolevel=0,mgal=1e11,dt=1e7,tend=1.3e10,imf_type='salpeter',alphaimf=2.35,\
imf_bdys=[1,30],iniZ=0,hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=False, \
sn1a_table='yield_tables/sn1a_h1.txt', iniabu_table='yield_tables/iniabu/iniab_h1.ppn')
Yield_tot_sim=s1.h... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Simulation results in the plot above should agree with semi-analytical calculations. Test of parameter imf_bdys: Selection of different initial mass intervals Select imf_bdys=[5,20] | k_N=1e11*0.35/ (5**-0.35 - 20**-0.35)
N_tot=k_N/1.35 * (5**-1.35 - 20**-1.35)
Yield_tot=0.1*N_tot
s1=s.sygma(iolevel=0,mgal=1e11,dt=1e9,tend=1.3e10,imf_type='salpeter',\
imf_bdys=[5,20],hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=False, \
sn1a_table='yield_tables/sn1a... | Sould be 1: 1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Select imf_bdys=[1,5] | k_N=1e11*0.35/ (1**-0.35 - 5**-0.35)
N_tot=k_N/1.35 * (1**-1.35 - 5**-1.35)
Yield_tot=0.1*N_tot
s1=s.sygma(iolevel=0,mgal=1e11,dt=1e9,tend=1.3e10,imf_type='salpeter',alphaimf=2.35,\
imf_bdys=[1,5],hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',\
sn1a_on=False, sn1a_table='yield_... | SYGMA run in progress..
SYGMA run completed - Run time: 0.32s
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Results: | print 'Sould be 1: ',Yield_tot_sim/Yield_tot | Sould be 1: 1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Test of parameter imf_type: Selection of different IMF types power-law exponent : alpha_imf The IMF allows to calculate the number of stars $N_{12}$ in the mass interval [m1,m2] with$N_{12}$ = k_N $\int _{m1}^{m2} m^{-alphaimf} dm$Where k_N is the normalization constant. It can be derived from the total amount of mas... | alphaimf = 1.5 #Set test alphaimf
k_N=1e11*(alphaimf-2)/ (-1**-(alphaimf-2) + 30**-(alphaimf-2))
N_tot=k_N/(alphaimf-1) * (-1**-(alphaimf-1) + 30**-(alphaimf-1))
Yield_tot=0.1*N_tot
s1=s.sygma(iolevel=0,mgal=1e11,dt=1e9,tend=1.3e10,imf_type='alphaimf',alphaimf=1.5,imf_bdys=[1,30],hardsetZ=0.0001,
table='yiel... | Should be 1 : 1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Chabrier: Change interval now from [0.01,30] M<1: $IMF(m) = \frac{0.158}{m} * \exp{ \frac{-(log(m) - log(0.08))^2}{2*0.69^2}}$else: $IMF(m) = m^{-2.3}$ | def imf_times_m(mass):
if mass<=1:
return 0.158 * np.exp( -np.log10(mass/0.079)**2 / (2.*0.69**2))
else:
return mass*0.0443*mass**(-2.3)
k_N= 1e11/ (quad(imf_times_m,0.01,30)[0] )
N_tot=k_N/1.3 * 0.0443* (1**-1.3 - 30**-1.3)
Yield_tot=N_tot * 0.1
s1=s.sygma(iolevel=0,mgal=1e11,dt=1e9,tend=1.3e10... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Simulation should agree with semi-analytical calculations for Chabrier IMF. Kroupa: M<0.08: $IMF(m) = m^{-0.3}$M<0.5 : $IMF(m) = m^{-1.3}$else : $IMF(m) = m^{-2.3}$ | def imf_times_m(mass):
p0=1.
p1=0.08**(-0.3+1.3)
p2=0.5**(-1.3+2.3)
p3= 1**(-2.3+2.3)
if mass<0.08:
return mass*p0*mass**(-0.3)
elif mass < 0.5:
return mass*p1*mass**(-1.3)
else: #mass>=0.5:
return mass*p1*p2*mass**(-2.3)
k_N= 1e11/ (quad(imf_times_m,0.01,30)[0] )
... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Simulation results compared with semi-analytical calculations for Kroupa IMF. Test of parameter sn1a_on: on/off mechanism | s1=s.sygma(iolevel=0,mgal=1e11,dt=1e7,tend=1.3e10,sn1a_on=False,sn1a_rate='maoz',imf_type='salpeter',
imf_bdys=[1,30],hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',
sn1a_table='yield_tables/sn1a_h1.txt', iniabu_table='yield_tables/iniabu/iniab_h1.ppn')
s2=s.sygma(iolevel=0,mgal... | [0] [0.0]
[100000000000.0] [3687728129.7190337]
[0] [10000000.000000006]
[100000000000.0] [3697728129.7190342]
1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Test of parameter sn1a_rate (DTD): Different SN1a rate implementatinos Calculate with SNIa and look at SNIa contribution only. Calculated for each implementation from $4*10^7$ until $1.5*10^{10}$ yrs DTD taken from Vogelsberger 2013 (sn1a_rate='vogelsberger') $\frac{N_{1a}}{Msun} = \int _t^{t+\Delta t} 1.3*10^{-3} ... | #import read_yields as ry
import sygma as s
reload(s)
plt.figure(99)
#interpolate_lifetimes_grid=s22.__interpolate_lifetimes_grid
#ytables=ry.read_nugrid_yields('yield_tables/isotope_yield_table_h1.txt')
#zm_lifetime_grid=interpolate_lifetimes_grid(ytables,iolevel=0) 1e7
s1=s.sygma(iolevel=0,mgal=1e11,dt=1e7,tend=1.3e1... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Small test: Initial mass vs. lifetime from the input yield grid compared to the fit in the the Mass-Metallicity-lifetime plane (done by SYGMA) for Z=0.02. A double integration has to be performed in order to solve the complex integral from Wiersma: | #following inside function wiersma09_efolding
#if timemin ==0:
# timemin=1
from scipy.integrate import dblquad
def spline1(x):
#x=t
minm_prog1a=3
#if minimum progenitor mass is larger than 3Msun due to IMF range:
#if self.imf_bdys[0]>3:
# minm_prog1a=self.imf_bdys[0]
return max(minm_prog... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Simulation results compared with semi-analytical calculations for the SN1 sources with Wiersma (exp) implementation. Compare number of WD's in range | sum(s1.wd_sn1a_range1)/sum(s1.wd_sn1a_range)
s1.plot_sn_distr(xaxis='time',fraction=False) | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Wiersmagauss | reload(s)
s2=s.sygma(iolevel=0,mgal=1e11,dt=1e7,tend=1.3e10,sn1a_rate='gauss',imf_type='salpeter',
imf_bdys=[1,30],hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=True,
sn1a_table='yield_tables/sn1a_h1.txt', iniabu_table='yield_tables/iniabu/iniab_h1.ppn')
Yield_tot_sim=... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Simulation results compared with semi-analytical calculations for the SN1 sources with Wiersma (Gauss) implementation. Compare number of WD's in range | sum(s2.wd_sn1a_range1)/sum(s2.wd_sn1a_range) | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
SNIa implementation: Maoz12 $t^{-1}$ | import sygma as s
reload(s)
s2=s.sygma(iolevel=0,mgal=1e11,dt=1e8,tend=1.3e10,sn1a_rate='maoz',imf_type='salpeter',
imf_bdys=[1,30],special_timesteps=-1,hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',
sn1a_on=True, sn1a_table='yield_tables/sn1a_h1.txt', iniabu_table='yield_table... | 10000000.0
10000000.0
Should be 1: 1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Check trend: | s2.plot_mass(fig=44,specie='H',source='sn1a',label='H',color='k',shape='-',marker='o',markevery=800)
yields1=[]
ages1=[]
m=[1,1.65,2,3,4,5,6,7,12,15,20,25]
ages=[5.67e9,1.211e9,6.972e8,2.471e8,1.347e8,8.123e7,5.642e7,4.217e7,1.892e7,1.381e7,9.895e6,7.902e6]
for m1 in m:
t=ages[m.index(m1)]
#yields= a* dblquad(w... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Test of parameter tend, dt and special_timesteps First constant timestep size of 1e7 | import sygma as s
s1=s.sygma(iolevel=0,mgal=1e11,dt=1e7,tend=1.3e10,special_timesteps=-1,imf_type='salpeter',
imf_bdys=[1,30],hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=False,
sn1a_table='yield_tables/sn1a_h1.txt', iniabu_table='yield_tables/iniabu/iniab_h1.ppn',
... | Should be 0: 0
Should be 1: 1.0
Should be 1: 1.0
Should be 1: 1.0
Should be 1: 1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
First timestep size of 1e7, then in log space to tend with a total number of steps of 200; Note: changed tend | import sygma as s
s2=s.sygma(iolevel=0,mgal=1e11,dt=1e7,tend=1.5e9,special_timesteps=200,imf_type='salpeter',
imf_bdys=[1,30],hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=False,
sn1a_table='yield_tables/sn1a_h1.txt', iniabu_table='yield_tables/iniabu/iniab_h1.ppn')
pr... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Choice of dt should not change final composition: for special_timesteps: | s3=s.sygma(iolevel=0,mgal=1e11,dt=1e7,tend=1.3e10,special_timesteps=-1,imf_type='salpeter',imf_bdys=[1,30],
hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=False, sn1a_table='yield_tables/sn1a_h1.txt',
iniabu_table='yield_tables/iniabu/iniab_h1.ppn',stellar_param_on=Fals... | should be 1 1.0
should be 1 1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Test of parameter mgal - the total mass of the SSP Test the total isotopic and elemental ISM matter at first and last timestep. | s1=s.sygma(iolevel=0,mgal=1e7,dt=1e7,tend=1.3e10,hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',
sn1a_on=False, sn1a_table='yield_tables/sn1a_h1.txt', iniabu_table='yield_tables/iniabu/iniab_h1.ppn')
s2=s.sygma(iolevel=0,mgal=1e8,dt=1e8,tend=1.3e10,hardsetZ=0.0001,table='yield_tables/agb_a... | At last timestep, should be the same fraction: 0.0170583657213 0.0170583657213 0.0170583657213
At last timestep, should be the same fraction: 0.0170583657213 0.0170583657213 0.0170583657213
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Test of SN rate: depend on timestep size: shows always mean value of timestep; larger timestep> different mean | reload(s)
s1=s.sygma(iolevel=0,mgal=1e11,dt=7e6,tend=1e8,imf_type='salpeter',imf_bdys=[1,30],hardsetZ=0.0001,
table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=True, sn1a_table='yield_tables/sn1a_h1.txt',
iniabu_table='yield_tables/iniabu/iniab_h1.ppn',pop3_table='yield_tables/popIII_h1.tx... | SYGMA run in progress..
SYGMA run completed - Run time: 0.3s
SYGMA run in progress..
SYGMA run completed - Run time: 11.46s
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Rate does not depend on timestep type: | s3.plot_sn_distr(fig=66,rate=True,rate_only='sn1a',label1='SN1a, rate',label2='SNII, rate',marker1='o',marker2='s',markevery=1)
s4.plot_sn_distr(fig=66,rate=True,rate_only='sn1a',label1='SN1a, number',label2='SNII number',marker1='d',marker2='p')
plt.xlim(3e7,1e10)
s1.plot_sn_distr(fig=77,rate=True,marker1='o',marker2=... | _____no_output_____ | BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
Test of parameter transitionmass : Transition from AGB to massive stars Check if transitionmass is properly set | import sygma as s; reload(s)
s1=s.sygma(iolevel=0,imf_bdys=[1.65,30],transitionmass=8,mgal=1e11,dt=1e7,tend=1.3e10,imf_type='salpeter',
hardsetZ=0.0001,table='yield_tables/agb_and_massive_stars_h1.txt',sn1a_on=False,
sn1a_table='yield_tables/sn1a_h1.txt', iniabu_table='yield_tables/iniabu/iniab_h... | 1: 1.0
1: 1.0
| BSD-3-Clause | regression_tests/.ipynb_checkpoints/SYGMA_SSP_h_yield_input-checkpoint.ipynb | katewomack/NuPyCEE |
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