Tag Archives: python
Lots of stuff for GO and Python: Important to know and read as well.
Python and Flask supports wide range of logging as well. Either it’s warning, error or just a logger you can ago through all of those in very specific instance of time.
Logging is important of the Maintainability of the application.
Now logging is something like you need to go for when you see or feel that your web app needs lots of “Watching as well!”
Here is simple Example in Flask:
from logging.handlers import RotatingFileHandler
from flask import Flask
app = Flask(__name__)
app.logger.warning('A warning occurred (%d apples)', 42)
app.logger.error('An error occurred')
if __name__ == '__main__':
handler = RotatingFileHandler('foo.log', maxBytes=10000, backupCount=1)
for more detailed view on logging and system you can go for the following link as well. :
It’s very much explanatory: https://gist.github.com/mariocj89/73824162a3e35d50db8e758a42e39aab
This is just first Quick and Fast Post.
TPOT Research Paper: https://arxiv.org/pdf/1702.01780.pdf
import datetime import numpy as np import pandas as pd import sklearn from pandas_datareader import data as read_data from tpot import TPOTClassifier from sklearn.model_selection import train_test_split apple_data = read_data.get_data_yahoo("AAPL") df = pd.DataFrame(index=apple_data.index) df['price']=apple_data.Open df['daily_returns']=df['price'].pct_change().fillna(0.0001) df['multiple_day_returns'] = df['price'].pct_change(3) df['rolling_mean'] = df['daily_returns'].rolling(window = 4,center=False).mean() df['time_lagged'] = df['price']-df['price'].shift(-2) df['direction'] = np.sign(df['daily_returns']) Y = df['direction'] X=df[['price','daily_returns','multiple_day_returns','rolling_mean']].fillna(0.0001) X_train, X_test, y_train, y_test = train_test_split(X,Y,train_size=0.75, test_size=0.25) tpot = TPOTClassifier(generations=50, population_size=50, verbosity=2) tpot.fit(X_train, y_train) print(tpot.score(X_test, y_test)) tpot.export('tpot_aapl_pipeline.py')
The Python file It returned: Which is real Code one can use to Create Trading Strategy. TPOT helped to Selected Algorithms and Value of It’s features. right now we have only provided ‘price’,’daily_returns’,’multiple_day_returns’,’rolling_mean’ to predict Target. One can use multiple features and implement as per the requirement.
import numpy as np import pandas as pd from sklearn.ensemble import GradientBoostingClassifier from sklearn.model_selection import train_test_split # NOTE: Make sure that the class is labeled 'target' in the data file tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64) features = tpot_data.drop('target', axis=1).values training_features, testing_features, training_target, testing_target = \ train_test_split(features, tpot_data['target'].values, random_state=42) # Score on the training set was:1.0 exported_pipeline = GradientBoostingClassifier(learning_rate=0.5, max_depth=7, max_features=0.7500000000000001, min_samples_leaf=11, min_samples_split=12, n_estimators=100, subsample=0.7500000000000001) exported_pipeline.fit(training_features, training_target) results = exported_pipeline.predict(testing_features)
Ok first of all when we think about OOPS we think about class.
#Object for class
#Access variable of class using object
#One more object
myandmy = Honey();
#assign new value to variable using object
myandmy.name = “yackity”;
For using function just use object with function name. like meandme.functionname(). so I think we can also use other stuff too in Python class. That’s All
python sikhni hai
SymPy is an open source Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.
The full features of sympy can be found here in the official page.. –
I want to share with you something more than that.here first i will tell you how to install sympy a full featured package as follows
- gmpy, Cython –> speed improvement
- Pyglet, Matplotlib –> 2d and 3d plotting
- IPython –> interactive sessions
gmpy,cython,pyglet,IPython can be used to extend this CAS functionality.Copy the following link in your browser to download the script for the automation in installation of complete sympy package.just download the script and save it on your desktop and run the script.
In my six week training i got the project related to sage math..Sage is a free open-source mathematics software system licensed under the GPL. It combines the power of many existing open-source packages into a common Python-based interface. my task was to complete study of sage and writing a python script for sage.
HOW TO INSTALL SAGE
1.)first of all download the sage binary from the link
2.)choose your mirror for downloading..well i used the following mirror for fast downloading
3.)select your version 32 bit or 64 bit.
4.)after the completion of download you will get a file with the extensions of .tar.lzma.it can be easily extracted in ubuntu linux using the command in terminal
“tar –lzma -xvf sage-*…tar.lzma” command
5.)if you are not so good with terminal then write click on the downloaded file and select extract here.
6.)when the file will be extracted completely open the terminal(alt+ctrl+t) and type following commands
cd /”path of the directory where sage file is located”/
cd /”name of the sage directory”/
if tou are able to run above commands successfully then in the terminal you will get the output like this. i downloaded file in the downloads directory and name of sage file is sage4.7
- then type “./sage” and you will able to run sage sucessfullly..if then any problem persist than first run command “sudo apt-get install gfortran” and then again again run start sage from terminal using “./sage”,, 🙂