def commit(notebook_id=None, nb_filename=None):
"""
sh： jovian clone notebook_id
:param nb_filename:
:param notebook_id: 内容覆盖566f95b138a9465aa8d17e0f1836570a -> https://jvn.io/Jie-Yuan/566f95b138a9465aa8d17e0f1836570a
:return:
"""
print(
"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJmcmVzaCI6ZmFsc2UsImlkZW50aXR5Ijp7InVzZXJuYW1lIjoiSmllLVl1YW4iLCJpZCI6Njd9LCJ0eXBlIjoiYWNjZXNzIiwiZXhwIjoxNTUyOTY1NTkzLCJpYXQiOjE1NTIzNjA3OTMsIm5iZiI6MTU1MjM2MDc5MywianRpIjoiNjM1ZTg2MjQtYjA1ZC00NGJmLTljYjAtOGVjOGRmM2ExNmJkIn0.5jglhEGGs12ITl-DWWaFL-BVPhCzaDEeMKIJvEI-bbA"
)
print('\n')
jovian.commit(nb_filename=nb_filename,
env_type='pip',
notebook_id=notebook_id)
testinputs, testlabels = testinput.to(device), testlabel.to(
device)
testpredictions = model9(testinputs)
_, testpredict = torch.max(testpredictions.data, 1)
tloss = criterion(testpredictions, testlabels)
testloss += tloss.item()
testtotal += testlabels.size(0)
testsuccessful += (testpredict == testlabels).sum().item()
trainlosses.append(trainloss / len(train_dl))
testlosses.append(testloss / len(valid_dl))
print('Epoch: ', e)
print('Train Accuracy %{:.2f}'.format(100 * trainsuccessful /
traintotal))
print('Test Accuracy %{:.2f}'.format(100 * testsuccessful / testtotal))
# In[ ]:
# In[ ]:
# In[ ]:
jovian.log_metrics(train_loss=history[-1]['train_loss'],
val_loss=history[-1]['val_loss'],
val_acc=history[-1]['val_acc'])
# In[80]:
jovian.commit(project=project_name, environment=None)
# In[ ]:
for i in range(0,1000):
if(y[i]==0):
pos=pos+1
else:
neg=neg+1
print(pos)
print(neg)
"""## Splitting the dataset into the Training set and Test set"""
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 = 0)
import jovian
jovian.commit(project='cmpn-100')
"""## Training the Naive Bayes model on the Training set"""
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)
"""## Training on Simple Logistic Regression"""
import jovian jovian.commit(message='first commit', files=['Basic Python.ipynb'])
import jovian
jovian.commit(project='jovian-v2-script-test2',
message='Testing message from script',
privacy='secret',
environment='pip',
git_message='a commit from jovian')
# In[3]:
project_name = "SuicideRates"
# In[4]:
get_ipython().system('pip install jovian --upgrade -q')
# In[5]:
import jovian
# In[6]:
jovian.commit(project=project_name)
# ## Loading the Dataset
# We are ready to load and read the dataset, above we used command "read_csv" which is used for read .csv format files. Now, we load the dataset and take a look.
# In[7]:
sr_df
# As we can see the data of dataset rows number and columns names
#
# In[8]:
sr_df.head(2)
The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on [mybinder.org](https://mybinder.org), a free online service for running Jupyter notebooks. Before staring the assignment, let's save a snapshot of the assignment to your Jovian.ml profile, so that you can access it later, and continue your work. """ # Install the library !pip install jovian --upgrade --quiet # Import it import jovian project_name='python-practice-assignment' # Capture and upload a snapshot jovian.commit(project=project_name, privacy='secret', evironment=None) """You'll be asked to provide an API Key, to securely upload the notebook to your Jovian.ml account. You can get the API key from your Jovian.ml profile page after logging in / signing up. See the docs for details: https://jovian.ml/docs/user-guide/upload.html . The privacy of your assignment notebook is set to *Secret*, so that you can the evlauators can access it, but it will not shown on your public profile to other users. ## Problem 1 - Variables and Data Types **Q: Assign your name to the variable `name`.** """ name = 'Purvansh' """**Q: Assign your age (real or fake) to the variable `age`.**""" age = 24 """**Q: Assign a boolean value to the variable `has_android_phone`.**"""
preds=model(inputs) loss=mse(preds,targets) print(loss) # In[27]: # Predictions preds # In[28]: # Targets targets # In[29]: import jovian # In[ ]: jovian.commit()
Qs = range(0, 2)
qs = range(0, 3)
Ps = range(0, 3)
ps = range(0, 3)
D=1
d=1
parameters = product(ps, qs, Ps, Qs)
parameters_list = list(parameters)
len(parameters_list)
# Model Selection
results = []
best_aic = float("inf")
warnings.filterwarnings('ignore')
for param in parameters_list:
try:
model=sm.tsa.statespace.SARIMAX(df_month.Weighted_Price_box, order=(param[0], d, param[1]),
seasonal_order=(param[2], D, param[3], 12)).fit(disp=-1)
except ValueError:
print('wrong parameters:', param)
continue
aic = model.aic
if aic < best_aic:
best_model = model
best_aic = aic
best_param = param
results.append([param, model.aic])
jovian.commit(project=project)
# In[1]:
get_ipython().system('pip install jovian --upgrade -q')
# In[2]:
import jovian
# In[3]:
jovian.commit(project='numpy-array-operations')
# Let's begin by importing Numpy and listing out the functions covered in this notebook.
# In[4]:
import numpy as np
# In[5]:
# List of functions explained
function1 = np.eye()
