vikramsoni2

import numpy as np
import pandas as pd
from multiprocessing import cpu_count, Pool, current_process
from functools import partial
from tqdm import tqdm

def split_df_by_group(_df, entity, chunks):  
    df_split=[]
    entities = _df[entity].unique()
    for i in range(chunks): df_split.append(_df[_df[entity].isin(entities[i::chunks])].copy())       

vikramsoni2

corr_matrix = all_data.corr().abs()
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
to_drop = [c for c in upper.columns if any(upper[c] > 0.95)]
del upper

drop_column = all_data.columns[to_drop]
print('drop columns:', drop_column)

vikramsoni2

from scipy import optimize


def calc_shifted_ewm(series, alpha, adjust=True):
    return series.shift().ewm(alpha=alpha, adjust=adjust).mean()


def find_best_signal(series, adjust=False, eps=10e-5):
    
    def f(alpha):

vikramsoni2

# actual code you can copy-paste into your Keras code to have Tensorflow dynamically allocate the GPU memory:

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True  # dynamically grow the memory used on the GPU
config.log_device_placement = True  # to log device placement (on which device the operation ran)
                                    # (nothing gets printed in Jupyter, only if you run it standalone)
sess = tf.Session(config=config)
set_session(sess)  # set this TensorFlow session as the default session for Keras

vikramsoni2

from bokeh.models import HoverTool
from bokeh.layouts import gridplot, column
from bokeh.plotting import figure, output_notebook, show, ColumnDataSource

from sklearn.metrics import precision_recall_curve

def fast_confusion_matrix(true_labels, predicted_labels, decimals=3, plot=True, only_best=False, min_recall=0):
    
    precision, recall, thresholds = precision_recall_curve(true_labels, predicted_labels)
    

vikramsoni2

def residual_plot(y_true, y_pred, **kwargs):

    line_end = np.max([df['O_target'].max(), df['prediction'].max()])

    p = figure(plot_width=900, plot_height=500, title="Residual Plot",
               x_axis_label='Actual', y_axis_label='Predicted', **kwargs )

    data = { 'Actual': y_true, 'Predicted': y_pred }

    p.circle(x='Actual', y='Predicted', source=data, fill_color='red', alpha=0.8, line_width=0.2, line_color='black')

vikramsoni2

PERCENTILES = [.005, .01, .025, .05, .10, .20, .25, .50, .75, .80, .90, .95, .975, .99]
def super_describe(_d: pd.DataFrame, percentiles: Iterable[float]=PERCENTILES, missing_only: bool=False) -> pd.DataFrame: 
    """
    Include counts of missing and of unique values.
    """
    if isinstance(_d, pd.Series):
        _d = _d.to_frame()
    _dd = pd.concat((_d.isnull().sum().rename('missing'), _d.nunique(axis=0).rename('unique'), _d.describe(percentiles=percentiles).T, ), axis=1, sort=False)
    return _dd.loc[_dd.missing > 0] if missing_only else _dd

vikramsoni2

Fundamentals of computer

What is computer?

A computer is an electronic device that manipulates information, or data. It has the ability to store, retrieve, and process data. It can process information in very fast speed. You can use a computer to type documents, send email play games, and browse the Web. You can also use it to edit or create spreadsheets, presentations, and even videos

---

Hardware

vikramsoni2

correlation = df.corr(method='pearson')
correlation = np.abs(correlation)*100

f, ax = plt.subplots(figsize=(30, 30))
cmap = sns.cubehelix_palette(light=1, as_cmap=True)

ax = sns.heatmap(correlation, cbar=False, annot=True, cmap=cmap, square=True, fmt='.0f',
            annot_kws={'size': 10})
plt.title('Corrlation Between Features')

vikramsoni2

{
	"version": "2",
	"templates": [
  {
    "type": 1,
    "title": "Airsonic",
    "name": "airsonic",
    "description": "Airsonic is a free, web-based media streamer, providing ubiqutious access to your music. Use it to share your music with friends, or to listen to your own music while at work. You can stream to multiple players simultaneously, for instance to one player in your kitchen and another in your living room.",
    "logo": "https://raw.githubusercontent.com/SelfhostedPro/selfhosted_templates/master/Images/airsonic-logo.png",
    "image": "linuxserver/airsonic:latest",