FMA: A Dataset For Music Analysis

Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson, EPFL LTS2. Free Music Archive web API All the data in the raw_*.csv tables was collected from the Free Music Archive public API. With this notebook, you can: reconstruct the original data, update some fields, e.g. the track listens (play count), augment the data with newer fields… Continue reading FMA: A Dataset For Music Analysis

word2vec visualization

using ipython notebook version: original In [1]: import visword2vec # 単語で作ったモデル vis = visword2vec.visWord2Vec(“recipe_steps.bin”) # フレーズで作ったモデル vis_phrase = visword2vec.visWord2Vec(“recipe_steps-phrase.bin”) loading loaded loading loaded In [2]: def plot_both(word): vis.plot(word) vis_phrase.plot(word) In [3]: plot_both(‘チョコ’) [ 0.21099427 0.11427726] [ 0.19734898 0.13841781] In [4]: vis_phrase.plot(‘義理_チョコ’) [ 0.16700684 0.1221358 ] In [5]: plot_both(‘義理’) [ 0.17986007 0.14094272] [ 0.248188 0.16636363] In [6]: plot_both(‘あつあつ’)… Continue reading word2vec visualization

How To Change User Password Using Django Admin

Snippet 1 ‘’’We can change the password using admin or superuser. Assuming you have super useralready created. Please add following urls in your‘’’from django.contrib import adminfrom django.urls import path, includeurlpatterns = [   path(‘admin/’,,   path(”, include (‘main.urls’)),   path(‘accounts/’, include(‘django.contrib.auth.urls’)),]‘’’In your browser go to url your your superuser name and password.Then click on users.Click… Continue reading How To Change User Password Using Django Admin

Multithreading With Queue In Python 3

Snippet 1 import pandas as pdimport threadingimport timefrom multiprocessing import Queuemy_queue = Queue(maxsize=0)#Let us define our workerdef worker(my_queue): #Add some time to fill up the queue time.sleep(5) while(my_queue.qsize() > 0):      entry = my_queue.get() #Let us define our threads herenum_threads = 10threads = []try:  for i in range(num_threads):      t = threading.Thread(target=worker,args=(my_queue)      t.start()     … Continue reading Multithreading With Queue In Python 3

Sqlalchemy Create Engine

from sqlalchemy import create_engine source engine = create_engine(“sqlite:///hawaii.sqlite”) source engine = create_engine(f’sqlite:///../../../ih_final_project_DB/ih_final_project’) con = engine.connect() nombre_tabla = engine.table_names() source def db_connection(path): engine = create_engine(path) connection = engine.connect() return connection source #endpoint = ‘sqlite:///insiders_db.sqlite’ #local endpoint = ‘sqlite:////Users/Alysson/Documents/Projects/Hotel-Booking-Cancelation/data/hotels.sqlite’ #local db = create_engine(endpoint, poolclass=NullPool) connection = db.connect() source protocol = ‘postgresql’ password=ETL_config.password username=ETL_config.username host = ‘localhost’ port… Continue reading Sqlalchemy Create Engine

Regression Trees

Estimated time needed: 20 minutes In this lab you will learn how to implement regression trees using ScikitLearn. We will show what parameters are important, how to train a regression tree, and finally how to determine our regression trees accuracy. Objectives After completing this lab you will be able to: Train a Regression Tree Evaluate… Continue reading Regression Trees

This is an IPython notebook, backed by a ruby kernel.

I wrote a kernel in Ruby that adheres to the IPython messaging protocol. Then I modified IPython, with the help of minrk from #ipython, to instantiate my Ruby Kernel instead of its own Python kernel. The IPython KernelManager start the RubyKernel as a subprocess, and from that point communication occurs over ZeroMQ, exactly as if… Continue reading This is an IPython notebook, backed by a ruby kernel.

Data Profiling

In [1]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_absolute_percentage_error import warnings warnings.filterwarnings(‘ignore’) In [2]: df = pd.read_csv(‘Salary_Data.csv’) In [3]: df.head() Out[3]: YearsExperience Salary 0 1.1… Continue reading Data Profiling

Stable Diffusion AI Notebook (Release 2.0.0)

Instructions: Execute each cell in order to mount a Dream bot and create images from text. Once cells 1-8 were run correctly you’ll be executing a terminal in cell #9, you’ll need to enter python scripts/ command to run Dream bot. After launching dream bot, you’ll see: Dream > in terminal. Insert a command, eg.… Continue reading Stable Diffusion AI Notebook (Release 2.0.0)

Time Series Forecasting with Python (ARIMA, LSTM, Prophet)

In [1]: import numpy as np import pandas as pd import os from statsmodels.tsa.statespace.sarimax import SARIMAX from import plot_acf,plot_pacf from statsmodels.tsa.seasonal import seasonal_decompose #from pmdarima import auto_arima from sklearn.metrics import mean_squared_error from import rmse import warnings warnings.filterwarnings(“ignore”) import matplotlib.pyplot as plt %matplotlib inline In this article we will try to forecast a time series… Continue reading Time Series Forecasting with Python (ARIMA, LSTM, Prophet)