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 |
|---|---|---|---|---|---|
Recommended to avoid The [documentation](https://github.com/HIPS/autograd/blob/master/docs/tutorial.md) recommends to avoid inplace operations such as | a += b
a -= b
a*= b
a /=b | _____no_output_____ | CC0-1.0 | doc/src/GradientOptim/autodiff/examples_allowed_functions.ipynb | ndavila/MachineLearningMSU |
B-Value estimates from Maximum LikelihoodHere we implement the maximum likelihood method from Tinti and Mulargia [1987]. We will compute the distribution of b-values from the stochastic event set and compare with the Comcat catalog. We will filter both the stochastic event sets and the catalog above Mw 3.95. | import time
import os
import pandas as pd
import numpy as np
import scipy.stats as stats
from csep.utils.plotting import plot_mfd
import csep
%pylab inline
def bval_ml_est(mws, dmw):
# compute the p term from eq 3.10 in marzocchi and sandri [2003]
def p():
top = dmw
# assuming that the magn... | -0.0620467887229116
| MIT | notes/maximum_likelihood.ipynb | thbeutin/csep2 |
Verifying computation of $a$ from Michael [2014]$log(N(m)) = a - bM$ $ a = log(N(m)/T) + bM $From Table 2 in Michael [2014], $T$: 1900 $-$ 2009 $M_c:$ 7.7 $N^{\prime}:$ 100 $b$ = 1.59 $\pm$ 0.13 | Np = 100
b = 1.59
Mc = 7.7
T = 2009-1900
sigma = 0.13
def a_val(N, M, b, T):
return np.log10(N/T) + M*b
a = a_val(Np, Mc, b, T)
print(a)
def a_err(a, b, sigma):
return a*sigma/b
print(a_err(a, b, sigma))
Np = 635
b = 1.07
Mc = 7.0
T = 2009-1918
sigma = 0.03
def a_val(N, M, b, T):
return np.log10(N/T) +... | 8.209635928141394
0.23456102651832553
| MIT | notes/maximum_likelihood.ipynb | thbeutin/csep2 |
List the available countries to download data for | pb.footballdata.list_countries() | _____no_output_____ | MIT | examples/football-data.co.uk.ipynb | martineastwood/penaltyblog |
Download the data for the English Premier League | pb.footballdata.fetch_data("England", 2020, 0) | _____no_output_____ | MIT | examples/football-data.co.uk.ipynb | martineastwood/penaltyblog |
Download the data for the French Ligue 2 | pb.footballdata.fetch_data("France", 2020, 1) | _____no_output_____ | MIT | examples/football-data.co.uk.ipynb | martineastwood/penaltyblog |
Investigate behavior of `curry` and `partial` | from toolz.curried import *
def clump3(a, b, c):
return a, b, c
@curry
def curried_clump3(a, b, c):
return a, b, c
partial1_clump3=partial(clump3, 1)
partial12_clump3=partial(clump3, 1, 2)
print(f'clump3(1, 2, 3)={clump3(1, 2, 3)}')
print(f'clump3(3, 1, 2)={clump3(3, 1, 2)}')
print()
print(f'curried_clump3(1)(2... | _____no_output_____ | MIT | curry_n_partial.ipynb | mrwizard82d1/learn_toolz |
Convolutional Neural Network (CNN) Image Classifier for Persian Numbers | import tensorflow as tf
from scipy.io import loadmat
import numpy as np
import matplotlib.pyplot as plt
import random
import math
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPool2D, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam... | _____no_output_____ | MIT | CNN_Persian_DigitsClassifier.ipynb | saniaki/Digit-Image-Classifier |
[HODA dataset](http://farsiocr.ir/%D9%85%D8%AC%D9%85%D9%88%D8%B9%D9%87-%D8%AF%D8%A7%D8%AF%D9%87/%D9%85%D8%AC%D9%85%D9%88%D8%B9%D9%87-%D8%A7%D8%B1%D9%82%D8%A7%D9%85-%D8%AF%D8%B3%D8%AA%D9%86%D9%88%DB%8C%D8%B3-%D9%87%D8%AF%DB%8C/) HODA Daset reader from: https://github.com/amir-saniyan/HodaDatasetReader | # *-* coding: utf-8 *-*
# Hoda Dataset Reader
# Python code for reading Hoda farsi digit dataset.
# Hoda Farsi Digit Dataset:
# http://farsiocr.ir/
# http://farsiocr.ir/مجموعه-داده/مجموعه-ارقام-دستنویس-هدی
# http://dadegan.ir/catalog/hoda
# Repository:
# https://github.com/amir-saniyan/HodaDatasetReader
import stru... | _____no_output_____ | MIT | CNN_Persian_DigitsClassifier.ipynb | saniaki/Digit-Image-Classifier |
Visualization fucntions | def show_images(n,image_array,label_array, cmap=None):
'''
show random n number of images from image_array with corresponding label_array
'''
total_rows = math.floor(n/4)+1
random_list = random.sample(range(0, image_array.shape[0]), n)
fig, axes = plt.subplots(total_rows, 4, figsize=(16, total_r... | _____no_output_____ | MIT | CNN_Persian_DigitsClassifier.ipynb | saniaki/Digit-Image-Classifier |
Check training images | n = 10 # number of images to show
# showing images and correspoind labels from train set
show_images(n,train_images,train_labels) | _____no_output_____ | MIT | CNN_Persian_DigitsClassifier.ipynb | saniaki/Digit-Image-Classifier |
CNN neural network classifier | def CNN_NN(input_shape, dropout_rate, reg_rate):
model = Sequential([
Conv2D(8, (3,3), activation='relu', input_shape=input_shape,
kernel_initializer="he_uniform", bias_initializer="ones",
kernel_regularizer=regularizers.l2(reg_rate), name='CONV2D_1_1_relu'),
BatchNor... | test accuracy: 0.983
test loss: 0.097
| MIT | CNN_Persian_DigitsClassifier.ipynb | saniaki/Digit-Image-Classifier |
Model predictions | def get_model_best_epoch(model, checkpoint_path):
'''
get model saved best epoch
'''
model.load_weights(checkpoint_path)
return model
# CNN model best epoch
model_CNN = CNN_NN(input_shape= (32,32,1), dropout_rate = 0.3, reg_rate=1e-4)
model_CNN = get_model_best_epoch(model_CNN, 'Trained models ... | _____no_output_____ | MIT | CNN_Persian_DigitsClassifier.ipynb | saniaki/Digit-Image-Classifier |
ComparisonTo do a comparison between MLP and CNN model, the MLP model is created here and the trained wights are loaded | def MLP_NN(input_shape, reg_rate):
'''
Multilayer Perceptron (MLP) classification model
'''
model = Sequential([
Flatten(input_shape=input_shape),
Dense(256, activation='relu', kernel_initializer="he_uniform", bias_initializer="ones",
kernel_regularizer=regularizers.l2(reg_... | _____no_output_____ | MIT | CNN_Persian_DigitsClassifier.ipynb | saniaki/Digit-Image-Classifier |
As a warm-up, you'll review some machine learning fundamentals and submit your initial results to a Kaggle competition. SetupThe questions below will give you feedback on your work. Run the following cell to set up the feedback system. | # Set up code checking
import os
if not os.path.exists("../input/train.csv"):
os.symlink("../input/home-data-for-ml-course/train.csv", "../input/train.csv")
os.symlink("../input/home-data-for-ml-course/test.csv", "../input/test.csv")
from learntools.core import binder
binder.bind(globals())
from learntools.... | _____no_output_____ | Apache-2.0 | notebooks/ml_intermediate/raw/ex1.ipynb | aurnik/learntools |
You will work with data from the [Housing Prices Competition for Kaggle Learn Users](https://www.kaggle.com/c/home-data-for-ml-course) to predict home prices in Iowa using 79 explanatory variables describing (almost) every aspect of the homes. Run the next ... | import pandas as pd
from sklearn.model_selection import train_test_split
# Read the data
X_full = pd.read_csv('../input/train.csv', index_col='Id')
X_test_full = pd.read_csv('../input/test.csv', index_col='Id')
# Obtain target and predictors
y = X_full.SalePrice
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlr... | _____no_output_____ | Apache-2.0 | notebooks/ml_intermediate/raw/ex1.ipynb | aurnik/learntools |
Use the next cell to print the first several rows of the data. It's a nice way to get an overview of the data you will use in your price prediction model. | X_train.head() | _____no_output_____ | Apache-2.0 | notebooks/ml_intermediate/raw/ex1.ipynb | aurnik/learntools |
Step 1: Evaluate several modelsThe next code cell defines five different random forest models. Run this code cell without changes. (_To review **random forests**, look [here](https://www.kaggle.com/dansbecker/random-forests)._) | from sklearn.ensemble import RandomForestRegressor
# Define the models
model_1 = RandomForestRegressor(n_estimators=50, random_state=0)
model_2 = RandomForestRegressor(n_estimators=100, random_state=0)
model_3 = RandomForestRegressor(n_estimators=100, criterion='mae', random_state=0)
model_4 = RandomForestRegressor(n_... | _____no_output_____ | Apache-2.0 | notebooks/ml_intermediate/raw/ex1.ipynb | aurnik/learntools |
To select the best model out of the five, we define a function `score_model()` below. This function returns the mean absolute error (MAE) from the validation set. Recall that the best model will obtain the lowest MAE. (_To review **mean absolute error**, look [here](https://www.kaggle.com/dansbecker/model-validation... | from sklearn.metrics import mean_absolute_error
# Function for comparing different models
def score_model(model, X_t=X_train, X_v=X_valid, y_t=y_train, y_v=y_valid):
model.fit(X_t, y_t)
preds = model.predict(X_v)
return mean_absolute_error(y_v, preds)
for i in range(0, len(models)):
mae = score_model(... | _____no_output_____ | Apache-2.0 | notebooks/ml_intermediate/raw/ex1.ipynb | aurnik/learntools |
Use the above results to fill in the line below. Which model is the best model? Your answer should be one of `model_1`, `model_2`, `model_3`, `model_4`, or `model_5`. | # Fill in the best model
best_model = ____
# Check your answer
step_1.check()
#%%RM_IF(PROD)%%
best_model = model_3
step_1.assert_check_passed()
# Lines below will give you a hint or solution code
#_COMMENT_IF(PROD)_
step_1.hint()
#_COMMENT_IF(PROD)_
step_1.solution() | _____no_output_____ | Apache-2.0 | notebooks/ml_intermediate/raw/ex1.ipynb | aurnik/learntools |
Step 2: Generate test predictionsGreat. You know how to evaluate what makes an accurate model. Now it's time to go through the modeling process and make predictions. In the line below, create a Random Forest model with the variable name `my_model`. | # Define a model
my_model = ____ # Your code here
# Check your answer
step_2.check()
#%%RM_IF(PROD)%%
my_model = 3
step_2.assert_check_failed()
#%%RM_IF(PROD)%%
my_model = best_model
step_2.assert_check_passed()
# Lines below will give you a hint or solution code
#_COMMENT_IF(PROD)_
step_2.hint()
#_COMMENT_IF(PROD)_
s... | _____no_output_____ | Apache-2.0 | notebooks/ml_intermediate/raw/ex1.ipynb | aurnik/learntools |
Run the next code cell without changes. The code fits the model to the training and validation data, and then generates test predictions that are saved to a CSV file. These test predictions can be submitted directly to the competition! | # Fit the model to the training data
my_model.fit(X, y)
# Generate test predictions
preds_test = my_model.predict(X_test)
# Save predictions in format used for competition scoring
output = pd.DataFrame({'Id': X_test.index,
'SalePrice': preds_test})
output.to_csv('submission.csv', index=False) | _____no_output_____ | Apache-2.0 | notebooks/ml_intermediate/raw/ex1.ipynb | aurnik/learntools |
时间转换及处理 str类型的时间转换为datetime类型的时间 | from datetime import datetime
time = '2010-05-01 00:00:00'
time = datetime.strptime(time, "%Y-%m-%d %H:%M:%S")
type(time),time | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
datetime类型的时间转换为str类型的时间 | from datetime import datetime
time = datetime(2010, 5, 1, 0, 0)
time = datetime.strftime(time, "%Y-%m-%d %H:%M:%S")
type(time),time
import tushare as ts
df = ts.get_k_data('600519','2020-08-01','2020-08-05')
df
type(df.date[140]),df.date[140]
import pandas as pd
#dateframe 日期数据，字符型转换成datetime日期格式
df.date = pd.to_dateti... | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
获取 日期数据 的年、月、日、时、分 | df.date.dt.time
df.date.dt.date
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
df.date.dt.year
df.date.dt.month
df.date.dt.day
df.date.dt.hour
df.date.dt.minute
df.date.dt.second | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
时间加减 | from datetime import datetime,timedelta
start = '2010-05-01 00:00:00'
start = datetime.strptime(start, "%Y-%m-%d %H:%M:%S")
time = start+timedelta(days=60)
time = datetime.strftime(time, "%Y-%m-%d %H:%M:%S")
time
from datetime import datetime,timedelta
start = '2010-05-01 00:00:00'
start = datetime.strptime(start, "%Y-... | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
dateime模块 datetime模块中包含如下类：  date类 today(...)：返回当前日期 | import datetime
datetime.date.today() | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
date对象由year年份、month月份及day日期三部分构成： | import datetime
a = datetime.date.today()
a
a.year,a.month,a.day | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
用于日期比较大小的方法  | a=datetime.date(2020,3,1)
b=datetime.date(2020,9,4)
a.__eq__(b)
a.__ge__(b)
a.__le__(b) | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
获得二个日期相差多少天  | a=datetime.date(2020,3,1)
b=datetime.date(2020,9,4)
a.__sub__(b).days
a.__rsub__(b).days | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
日期的字符串输出 | import datetime
a = datetime.date.today()
a.__format__('%Y-%m-%d')
import datetime
a = datetime.date.today()
a.__format__('%Y/%m/%d')
import datetime
a = datetime.date.today()
a.__str__() | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
time类 time类由hour小时、minute分钟、second秒、microsecond毫秒和tzinfo五部分组成 | import datetime
a = datetime.time(12,20,59,899)
a.__str__()
a.__format__('%H:%M:%S') | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
datetime类 datetime类其实是可以看做是date类和time类的合体，其大部分的方法和属性都继承于这二个类 返回现在的时间 | import datetime
a = datetime.datetime.now()
a
a.date(),a.time() | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
combine(…)：将一个date对象和一个time对象合并生成一个datetime对象 | datetime.datetime.combine(a.date(),a.time()) | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
strptime(…)：根据string, format 2个参数，返回一个对应的datetime对象： | datetime.datetime.strptime('2017-3-22 15:25','%Y-%m-%d %H:%M') | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
strftime(…)：根据datetime, format 的参数，返回一个对应的str： | a = datetime.datetime.now()
datetime.datetime.strftime(a,'%Y-%m-%d %H:%M:%S') | _____no_output_____ | MIT | .ipynb_checkpoints/1.6 时间转换及处理-checkpoint.ipynb | Yanie1asdfg/Quant-Lectures |
Torrent To Google Drive Downloader **Important Note:** To get more disk space:> Go to Runtime -> Change Runtime and give GPU as the Hardware Accelerator. You will get around 384GB to download any torrent you want. Install libtorrent and Initialize Session | !python -m pip install --upgrade pip setuptools wheel
!python -m pip install lbry-libtorrent
!apt install python3-libtorrent
import libtorrent as lt
ses = lt.session()
ses.listen_on(6881, 6891)
downloads = [] | _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | l-i-e-d-j-i-6-7-8-w-d/Torrent-To-Google-Drive-Downloader |
Mount Google DriveTo stream files we need to mount Google Drive. | from google.colab import drive
drive.mount("/content/drive") | _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | l-i-e-d-j-i-6-7-8-w-d/Torrent-To-Google-Drive-Downloader |
Add From Torrent FileYou can run this cell to add more files as many times as you want | from google.colab import files
source = files.upload()
params = {
"save_path": "/content/drive/My Drive/Torrent",
"ti": lt.torrent_info(list(source.keys())[0]),
}
downloads.append(ses.add_torrent(params)) | _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | l-i-e-d-j-i-6-7-8-w-d/Torrent-To-Google-Drive-Downloader |
Add From Magnet LinkYou can run this cell to add more files as many times as you want | params = {"save_path": "/content/drive/My Drive/Torrent"}
while True:
magnet_link = input("Enter Magnet Link Or Type Exit: ")
if magnet_link.lower() == "exit":
break
downloads.append(
lt.add_magnet_uri(ses, magnet_link, params)
)
| _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | l-i-e-d-j-i-6-7-8-w-d/Torrent-To-Google-Drive-Downloader |
Start DownloadSource: https://stackoverflow.com/a/5494823/7957705 and [3 issue](https://github.com/FKLC/Torrent-To-Google-Drive-Downloader/issues/3) which refers to this [stackoverflow question](https://stackoverflow.com/a/6053350/7957705) | import time
from IPython.display import display
import ipywidgets as widgets
state_str = [
"queued",
"checking",
"downloading metadata",
"downloading",
"finished",
"seeding",
"allocating",
"checking fastresume",
]
layout = widgets.Layout(width="auto")
style = {"description_width": "ini... | _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | l-i-e-d-j-i-6-7-8-w-d/Torrent-To-Google-Drive-Downloader |
Monte Carlo MethodsIn this notebook, you will write your own implementations of many Monte Carlo (MC) algorithms. While we have provided some starter code, you are welcome to erase these hints and write your code from scratch. Part 0: Explore BlackjackEnvWe begin by importing the necessary packages. | import sys
import gym
import numpy as np
from collections import defaultdict
from plot_utils import plot_blackjack_values, plot_policy | _____no_output_____ | MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Use the code cell below to create an instance of the [Blackjack](https://github.com/openai/gym/blob/master/gym/envs/toy_text/blackjack.py) environment. | env = gym.make('Blackjack-v0') | _____no_output_____ | MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Each state is a 3-tuple of:- the player's current sum $\in \{0, 1, \ldots, 31\}$,- the dealer's face up card $\in \{1, \ldots, 10\}$, and- whether or not the player has a usable ace (`no` $=0$, `yes` $=1$).The agent has two potential actions:``` STICK = 0 HIT = 1```Verify this by running the code cell below. | print(env.observation_space)
print(env.action_space) | Tuple(Discrete(32), Discrete(11), Discrete(2))
Discrete(2)
| MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Execute the code cell below to play Blackjack with a random policy. (_The code currently plays Blackjack three times - feel free to change this number, or to run the cell multiple times. The cell is designed for you to get some experience with the output that is returned as the agent interacts with the environment._) | for i_episode in range(3):
state = env.reset()
while True:
print(state)
action = env.action_space.sample()
state, reward, done, info = env.step(action)
if done:
print('End game! Reward: ', reward)
print('You won :)\n') if reward > 0 else print('You lost :(... | (12, 10, False)
End game! Reward: -1.0
You lost :(
(19, 10, False)
End game! Reward: -1
You lost :(
(18, 2, False)
End game! Reward: -1
You lost :(
| MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Part 1: MC PredictionIn this section, you will write your own implementation of MC prediction (for estimating the action-value function). We will begin by investigating a policy where the player _almost_ always sticks if the sum of her cards exceeds 18. In particular, she selects action `STICK` with 80% probability ... | def generate_episode_from_limit_stochastic(bj_env):
episode = []
state = bj_env.reset()
while True:
probs = [0.8, 0.2] if state[0] > 18 else [0.2, 0.8]
action = np.random.choice(np.arange(2), p=probs)
next_state, reward, done, info = bj_env.step(action)
episode.append((state,... | _____no_output_____ | MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Execute the code cell below to play Blackjack with the policy. (*The code currently plays Blackjack three times - feel free to change this number, or to run the cell multiple times. The cell is designed for you to gain some familiarity with the output of the `generate_episode_from_limit_stochastic` function.*) | for i in range(3):
print(generate_episode_from_limit_stochastic(env)) | [((17, 9, False), 1, -1)]
[((16, 6, False), 1, -1)]
[((18, 8, False), 1, -1)]
| MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Now, you are ready to write your own implementation of MC prediction. Feel free to implement either first-visit or every-visit MC prediction; in the case of the Blackjack environment, the techniques are equivalent.Your algorithm has three arguments:- `env`: This is an instance of an OpenAI Gym environment.- `num_episo... | def mc_prediction_q(env, num_episodes, generate_episode, gamma=1.0):
# initialize empty dictionaries of arrays
returns_sum = defaultdict(lambda: np.zeros(env.action_space.n))
N = defaultdict(lambda: np.zeros(env.action_space.n))
Q = defaultdict(lambda: np.zeros(env.action_space.n))
# loop over episo... | [((19, 10, False), 0, 1.0)]
states: ((19, 10, False),)
actions: (0,)
rewards: (1.0,)
| MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Use the cell below to obtain the action-value function estimate $Q$. We have also plotted the corresponding state-value function.To check the accuracy of your implementation, compare the plot below to the corresponding plot in the solutions notebook **Monte_Carlo_Solution.ipynb**. | # obtain the action-value function
Q = mc_prediction_q(env, 500000, generate_episode_from_limit_stochastic)
# obtain the corresponding state-value function
V_to_plot = dict((k,(k[0]>18)*(np.dot([0.8, 0.2],v)) + (k[0]<=18)*(np.dot([0.2, 0.8],v))) \
for k, v in Q.items())
# plot the state-value function
plot_b... | Episode 500000/500000. | MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Part 2: MC ControlIn this section, you will write your own implementation of constant-$\alpha$ MC control. Your algorithm has four arguments:- `env`: This is an instance of an OpenAI Gym environment.- `num_episodes`: This is the number of episodes that are generated through agent-environment interaction.- `alpha`: Th... | import random
def generate_episode_from_q(env, Q, nA, epsilon):
epsidoe = []
state = env.reset()
while True:
# from the reset state, we can make choice or sample randamly
action = np.random.choice(np.arange(nA), p=get_probs(Q, nA, epsilon)) if state in Q else env.action_space.sample()
... | _____no_output_____ | MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Use the cell below to obtain the estimated optimal policy and action-value function. Note that you should fill in your own values for the `num_episodes` and `alpha` parameters. | # obtain the estimated optimal policy and action-value function
policy, Q = mc_control(env, 500000, 0.02) | Episode 500000/500000. | MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Next, we plot the corresponding state-value function. | # obtain the corresponding state-value function
V = dict((k,np.max(v)) for k, v in Q.items())
# plot the state-value function
plot_blackjack_values(V) | _____no_output_____ | MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
Finally, we visualize the policy that is estimated to be optimal. | # plot the policy
plot_policy(policy) | _____no_output_____ | MIT | 2-Valued-Based Methods/monte-carlo/Monte_Carlo.ipynb | zhaolongkzz/DRL-of-Udacity |
To Do1. Try different architectures2. Try stateful/stateless LSTM.3. Add OAT, holidays.4. Check if data has consecutive blocks. | import numpy as np
import pandas as pd
from scipy import stats
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.callbacks import EarlyStopping
from keras.layers import Dropout, Dense, LSTM
from statsmodels.tsa.stattools im... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Import data Power data | df_power = pd.read_csv(power_data_folder + '/power_' + site + '.csv', index_col=[0], parse_dates=True)
df_power.columns = ['power']
df_power.head()
df_power.plot(figsize=(18,5)) | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Check for missing data | df_power.isna().any() | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Clean data | # Resample to 5min
df_processed = df_power.resample('5T').mean()
df_processed.head()
df_processed.plot(figsize=(18,5)) | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Check for missing data | print(df_processed.isna().any())
print('\n')
missing = df_processed['power'].isnull().sum()
total = df_processed['power'].shape[0]
print('% Missing data for power: ', (missing/total)*100, '%') | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Depending on the percent missing data, either drop it or forward fill the NaN's | # Option 1: Drop NaN's
df_processed.dropna(inplace=True)
# # Option 2: ffill NaN's
# df_processed = df_processed.fillna(method='ffill') | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Normalize data | scaler = MinMaxScaler(feature_range=(0,1))
df_normalized = pd.DataFrame(scaler.fit_transform(df_processed),
columns=df_processed.columns, index=df_processed.index)
df_normalized.head() | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Check for missing data | df_normalized.isna().any() | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Check for stationarity | result = adfuller(df_normalized['power'], autolag='AIC')
output = pd.Series(result[0:4], index=['Test Statistic', 'p-value', '#Lags Used',
'#Observations Used'])
for key, value in result[4].items():
output['Critical Value (%s)' % key] = value
output | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
HVAC States data | df_hvac_states = pd.read_csv(hvac_states_data_folder + '/hvac_states_' + site + '.csv',
index_col=[0], parse_dates=True)
df_hvac_states.columns = ['zone' + str(i) for i in range(len(df_hvac_states.columns))]
df_hvac_states.head() | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Check for missing data | df_hvac_states.isna().any() | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Convert categorical (HVAC states) into dummy variables | var_to_expand = df_hvac_states.columns
# One-hot encode the HVAC states
for var in var_to_expand:
add_var = pd.get_dummies(df_hvac_states[var], prefix=var, drop_first=True)
# Add all the columns to the model data
df_hvac_states = df_hvac_states.join(add_var)
# Drop the original column that was expan... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Join power and hvac_states data | # CHECK: pd.concat gives a lot of duplicate indices.
# Try below code to see,
# start = pd.Timestamp('2018-02-10 06:00:00+00:00')
# df.loc[start]
df = pd.concat([df_normalized, df_hvac_states], axis=1)
df.head()
df = df.drop_duplicates()
missing = df.isnull().sum()
total = df.shape[0]
print('missing data for power: '... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Depending on the percent missing data, either drop it or forward fill the NaN's | # Option 1: Drop NaN's
df.dropna(inplace=True)
# # Option 2: ffill NaN's
# df = df.fillna(method='ffill') | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Visualizations Box plot | df_box_plot = pd.DataFrame(df['power'])
df_box_plot['quarter'] = df_box_plot.index.quarter
df_box_plot.boxplot(column='power', by='quarter') | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Histogram | df['power'].hist() | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
ACF and PACF | fig1 = plot_acf(df_processed['power'], lags=50)
fig2 = plot_pacf(df_processed['power'], lags=50) | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Prepare data Split into training & testing data | X_train = df[(df.index < '2019-01-01')]
y_train = df.loc[(df.index < '2019-01-01'), 'power']
X_test = df[(df.index >= '2019-01-01')]
y_test = df.loc[(df.index >= '2019-01-01'), 'power'] | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Prepare data for LSTMNote: NUM_TIMESTEPS is a hyper-parameter too! | # Number of columns in X_train
NUM_FEATURES = len(X_train.columns)
# A sequence contains NUM_TIMESTEPS number of elements and predicts NUM_MODEL_PREDICTIONS number of predictions
NUM_TIMESTEPS = 24
# Since this is an iterative method, model will predict only 1 timestep ahead
NUM_MODEL_PREDICTIONS = 1
# 4 hour p... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
LSTM | model = Sequential([
LSTM(units=128, input_shape=(NUM_TIMESTEPS, NUM_FEATURES), return_sequences=True),
Dropout(0.2),
LSTM(units=128, return_sequences=True),
Dropout(0.2),
LSTM(units=128, activation='softmax', return_sequences=False),
Dropout(0.2),
Dense(NUM_MODEL_PREDICT... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Results Loss | train_loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = [x for x in range(len(train_loss))]
df_train_loss = pd.DataFrame(train_loss, columns=['train_loss'], index=epochs)
df_val_loss = pd.DataFrame(val_loss, columns=['val_loss'], index=epochs)
df_loss = pd.concat([df_train_loss, df_val_... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Accuracy | train_acc = history.history['acc']
val_acc = history.history['val_acc']
epochs = [x for x in range(len(train_acc))]
df_train_acc = pd.DataFrame(train_acc, columns=['train_acc'], index=epochs)
df_val_acc = pd.DataFrame(val_acc, columns=['val_acc'], index=epochs)
df_acc = pd.concat([df_train_acc, df_val_acc], ax... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Plot predicted & true values | # Make predictions through trained model
pred_y = model.predict(test_x)
# Convert predicted and actual values to dataframes (for plotting)
df_y_pred = pd.DataFrame(scaler.inverse_transform(pred_y),
index=y_test[NUM_TIMESTEPS:-NUM_MODEL_PREDICTIONS].index,
columns=['po... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Make predictions through iterative fitting for a particular timestamp Choose a particular timestamp | timestamp = pd.Timestamp('2019-01-01 23:45:00+00:00')
# Keep copy of timestamp to use it after the for loop
orig_timestamp = timestamp
X_test_pred = X_test.copy()
for _ in range(NUM_ACTUAL_PREDICTIONS):
# Create test sequence
test = np.array(X_test_pred.loc[:timestamp].tail(NUM_TIMESTEPS))
test = np.... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Plot | arr_pred = np.reshape(X_test_pred.loc[orig_timestamp:,'power'].head(NUM_ACTUAL_PREDICTIONS).values, (-1, 1))
arr_true = np.reshape(X_test.loc[orig_timestamp:,'power'].head(NUM_ACTUAL_PREDICTIONS).values, (-1, 1))
df_pred = pd.DataFrame(scaler.inverse_transform(arr_pred),
index=X_test_pred.loc[... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
Get accuracy and mse of the entire test set using iterative fittingNote: This takes a while to compute! | # These two lists store the entire dataframes of 48 predictions of each element in test set!
# This is not really necessary but only to double check if the outputs are in the correct format
predicted_values = []
true_values = []
for i in range(NUM_TIMESTEPS, len(X_test)-NUM_ACTUAL_PREDICTIONS):
# Keep copy of... | _____no_output_____ | BSD-2-Clause | services/energy_consumption_forecast/lstm/LSTM (Iterative).ipynb | phgupta/XBOS |
**Project 4 Notebook 1****Data Acquisition**Using the Google Chrome web browser extension "Web Scraper", I scraped stories and other data from Fanfiction.net. I searched for Hunger Games stories, filtering for stories that were rated T, and that had Katniss Everdeen (there are 4 fields where you can put characters, and... | import numpy as np
import nltk
import pandas as pd | _____no_output_____ | MIT | Notebook-1-Project-4-Hunger-Games-Fanfiction-webscraping.ipynb | sutrofog/Sillman-Metis-Project4 |
The data was scraped in two batches and saved in .csv files. I read in the two files, created Pandas DataFrames, and then joined the two DataFrames using append. | data = pd.read_csv('Project-4-data/fanfiction-katniss1_pre_page_69.csv')
data.head()
data.info()
data2=pd.read_csv('Project-4-data/fanfiction-katniss1_p69-end_complete.csv')
data.head()
data2.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 1718 entries, 0 to 1717
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 web-scraper-order 1718 non-null object
1 web-scraper-start-url 1718 non-null object
2 stor... | MIT | Notebook-1-Project-4-Hunger-Games-Fanfiction-webscraping.ipynb | sutrofog/Sillman-Metis-Project4 |
Append the dataframes to make a dataframe with the complete dataset. | katniss=data.append(data2)
katniss.head()
katniss.info() | <class 'pandas.core.frame.DataFrame'>
Int64Index: 3443 entries, 0 to 1717
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 web-scraper-order 3443 non-null object
1 web-scraper-start-url 3443 non-null object
2 stor... | MIT | Notebook-1-Project-4-Hunger-Games-Fanfiction-webscraping.ipynb | sutrofog/Sillman-Metis-Project4 |
Removed some unnecessary columns. | ##Can delete columns "previous_pages" and "next_pages".
##These are links that the scraping extension put in.
katniss.drop(["previous_pages", "previous_pages-href",
"next_pages", "next_pages-href"], axis=1, inplace=True )
katniss.head()
katniss.info()
#replace punctuation with a white space, remove number... | _____no_output_____ | MIT | Notebook-1-Project-4-Hunger-Games-Fanfiction-webscraping.ipynb | sutrofog/Sillman-Metis-Project4 |
I can delete a couple columns to save space. 'story_text' and 'story_text_no_quotes'using: >katniss.drop(["story_text", "story_text_no_quotes"], axis=1, inplace=True ) | #katniss.to_csv('katniss-word-tokenized_only.csv')
katniss.head()
#Now I can try to take out the stopwords.
katniss['story_text_without_stopwords'] = katniss['story_text'].apply(lambda x: [item for item in x if item not in stop])
katniss.head()
#Super! It worked! Save it as a .csv
katniss.to_csv('katniss-wtok-no-stops-... | _____no_output_____ | MIT | Notebook-1-Project-4-Hunger-Games-Fanfiction-webscraping.ipynb | sutrofog/Sillman-Metis-Project4 |
Homework 2Cross Validation Problem In this homework, you will use cross validation to analyze the effect on model qualityof the number of model parameters and the noise in the observational data.You do this analysis in the context of design of experiments.The two factors are (i) number of model parameters and (ii) the... | IS_COLAB = False
#
if IS_COLAB:
!pip install tellurium
!pip install SBstoat
#
# Constants for standalone notebook
if not IS_COLAB:
CODE_DIR = "/home/ubuntu/advancing-biomedical-models/common"
else:
from google.colab import drive
drive.mount('/content/drive')
CODE_DIR = "/content/drive/My Drive/W... | _____no_output_____ | MIT | assignments/Homework2.ipynb | BioModelTools/topics-course |
Now You Code 4: Syracuse WeatherWrite a program to load the Syracuse weather data from Dec 2015 inJSON format into a Python list of dictionary. The file with the weather data is in your `Now-You-Code` folder: `"NYC4-syr-weather-dec-2015.json"`You should load this data into a Python list of dictionary using the `json` ... | # Step 2: Write code
import json
def load_weather_data():
with open('NYC4-syr-weather-dec-2015.json') as f:
data = f.read()
weather = json.loads(data)
return weather
def extract_weather_info(weather):
info = {}
info['mean temp'] = weather['Mean TemperatufeF']
info['high... | _____no_output_____ | MIT | content/lessons/10/Now-You-Code/NYC4-Syracuse-Weather.ipynb | jferna22-su/ist256 |
Постановка задачиРассмотрим несколько моделей линейной регрессии, чтобы выяснить более оптимальную для первых 20 зданий.Данные:* http://video.ittensive.com/machine-learning/ashrae/building_metadata.csv.gz* http://video.ittensive.com/machine-learning/ashrae/weather_train.csv.gz* http://video.ittensive.com/machine-learn... | import pandas as pd
from pandas.tseries.holiday import USFederalHolidayCalendar as calendar
import numpy as np
from scipy.interpolate import interp1d
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet, BayesianRidge
def reduce_mem_usage (df):... | _____no_output_____ | MIT | ASHRAE/competitive_reg_models.ipynb | Costigun/kaggle_practice |
Линейная регрессия\begin{equation}z = Ax + By + C, |z-z_0|^2 \rightarrow min\end{equation}Лассо + LARS Лассо\begin{equation}\frac{1}{2n}|z-z_0|^2 + a(|A|+|B|) \rightarrow min\end{equation}Гребневая регрессия\begin{equation}|z-z_0|^2 + a(A^2 + B^2) \rightarrow min\end{equation}ElasticNet: Лассо + Гребневая регрессия\beg... | lr_models = {
"LinearRegression":LinearRegression,
"Lasso-0.01":Lasso,
"Lasso-0.1":Lasso,
"Lasso-1.0":Lasso,
"Ridge-0.01":Ridge,
"Ridge-0.1":Ridge,
"Ridge-1.0":Ridge,
"ELasticNet-1-1":ElasticNet,
"ELasticNet-0.1-1":ElasticNet,
"ELasticNet-1-0.1":ElasticNet,
"ELasticNet-0.1-0.... | [-0.05204313 5.44504565 5.41921165 5.47881611 5.41753305 5.43838778
5.45137392 5.44059806]
[-0.04938976 5.44244413 5.41674949 5.47670968 5.41516617 5.43591691
5.44949479 5.43872264]
[-0.05138182 5.44439819 5.41859905 5.47829205 5.41694412 5.43777302
5.45090643 5.44013149]
| MIT | ASHRAE/competitive_reg_models.ipynb | Costigun/kaggle_practice |
import matplotlib.pyplot as mpl
import matplotlib.ticker as plticker
import numpy as np
from scipy.optimize import minimize_scalar
import pathlib
if not pathlib.Path("mpl_utils.py").exists():
!curl -O https://raw.githubusercontent.com/joaochenriques/MCTE_2022/main/libs/mpl_utils.py &> /dev/null
import mpl_utils as m... | _____no_output_____ | MIT | ChannelFlows/Simulation/ChannelFlowSimulation.ipynb | joaochenriques/MCTE_2022 | |
**Setup the problem** | ρw = 1025 # [kg/m³] salt water density
g = 9.8 # [m/s²] gravity aceleration
T = 12.0*3600.0 + 25.2*60.0 # [s] tide period
L = 20000 # [m] channel length
h = 60 # [m] channel depth
b = 4000 # [m] channel width
a = 1.2 # [m] tidal amplitude
S = h*b # [m²] channel area
twopi = 2*np.pi
... | _____no_output_____ | MIT | ChannelFlows/Simulation/ChannelFlowSimulation.ipynb | joaochenriques/MCTE_2022 |
**Solution of the ODE**$\displaystyle \frac{dQ^*}{dt^*}=\cos(t^*) - (\Theta_\text{f}^*+BC_\text{T} \Theta_\text{T}^*) \, Q^* \, |Q^*|$$\displaystyle \frac{d E_\text{T}^*}{dt^*}= BC_\text{P} \, |{Q^*}^3|$where $B$, $\Theta_\text{f}^*$ and $\Theta_\text{T}^*$ are constants, and $C_\text{T}$ and $C_\text{P}$ are computed... | def f_star( ys, ts, Θ_f_star, Θ_T_star, Fr_0, B_rows ):
( Q_star, E_star ) = ys
BC_T_rows = np.zeros( len( B_rows ) )
BC_P_rows = np.zeros( len( B_rows ) )
B_0 = np.nan
for j, B in enumerate( B_rows ):
# do not repeat the computations if B is equal to the previous iteration
if B_... | _____no_output_____ | MIT | ChannelFlows/Simulation/ChannelFlowSimulation.ipynb | joaochenriques/MCTE_2022 |
**Solution with channel bed friction and turbines thrust** | periods = 4
ppp = 100 # points per period
num = int(ppp*periods)
# stores time vector
ts_vec = np.linspace( 0, (2*np.pi) * periods, num )
Delta_ts = ts_vec[1] - ts_vec[0]
# vector that stores the lossless solution time series
ys_lossless_vec = np.zeros( ( num, 2 ) )
# solution of (Eq. 3) without "friction" term
for... | _____no_output_____ | MIT | ChannelFlows/Simulation/ChannelFlowSimulation.ipynb | joaochenriques/MCTE_2022 |
The blockage factor per turbine row $i$ is$$B_i=\displaystyle \frac{\left( n_\text{T} A_\text{T}\right)_i}{S_i}$$where $\left( n_\text{T} A_\text{T}\right)_i$ is the area of all turbines of row $i$, and $S_i$ is the cross-sectional area of the channel at section $i$. | fig, (ax1, ax2) = mpl.subplots(1,2, figsize=(12, 4.5) )
fig.subplots_adjust( wspace = 0.17 )
B_local = 0.1
n_step = 18
for n_mult in tqdm( ( 0, 1, 2, 4, 8, 16 ) ):
n_rows = n_step * n_mult
B_rows = [B_local] * n_rows
# vector that stores the solution time series
ys_vec = np.zeros( ( num, 2 ) )
#... | _____no_output_____ | MIT | ChannelFlows/Simulation/ChannelFlowSimulation.ipynb | joaochenriques/MCTE_2022 |
**Plot the solution as function of the number of turbines** | n_rows_lst = range( 0, 512+1, 8 ) # number of turbines [-]
Ps_lst = []
B_local = 0.1
ys1_vec = np.zeros( ( num, 2 ) )
for n_rows in tqdm( n_rows_lst ):
B_rows = [B_local]*n_rows
# solution of (Eq. 3) with "friction" terms
# the initial conditions are always (0,0)
for i, ts in enumerate( ts_vec[1:] ):
... | _____no_output_____ | MIT | ChannelFlows/Simulation/ChannelFlowSimulation.ipynb | joaochenriques/MCTE_2022 |
**TOOLS FOR DEMOGRAPHY** Token y Drive | # Token para GEE
import ee
from google.colab import auth
auth.authenticate_user()
ee.Authenticate()
ee.Initialize()
# Vincular con Drive
from google.colab import drive
drive.mount('/content/drive') | Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
| MIT | Phyton/Manejo_Datos.ipynb | jcms2665/100-Days-Of-ML-Code |
Manejo de Bases de Datos--- | # Instalación de paquetes
!pip install pyreadstat
!pip install simpledbf
# Cargar paquetes
import os # Directorios
import csv
import matplotlib.pyplot as plt
import numpy as np # Data frame
import pandas as pd
import pyreadstat
os.getcwd()
a="/content/drive/MyDr... | _____no_output_____ | MIT | Phyton/Manejo_Datos.ipynb | jcms2665/100-Days-Of-ML-Code |
Table of Contents1 Exploratory data analysis1.1 Desribe data1.1.1 Sample size1.1.2 Descriptive statistics1.1.3 Shapiro-Wilk Test1.1.4 Histograms1.2 Kendall's Tau correlation1.3 Correlation Heatmap | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import shapiro, kendalltau
from sklearn import linear_model
import statsmodels.api as sm
df = pd.read_csv('data/cleaned_data_gca.csv') | _____no_output_____ | MIT | 210601 gca data analyses.ipynb | rbnjd/gca_data_analyses |
Exploratory data analysis Desribe data Sample size | print('Sample size socio-demographics =', df[df.columns[0]].count())
print('Sample size psychological variables =', df[df.columns[4]].count()) | Sample size socio-demographics = 33
Sample size psychological variables = 34
| MIT | 210601 gca data analyses.ipynb | rbnjd/gca_data_analyses |
Descriptive statistics **Descriptive statistics for numeric data** | descriptive_stat = df.describe()
descriptive_stat = descriptive_stat.T
descriptive_stat['skew'] = df.skew()
descriptive_stat['kurtosis'] = df.kurt()
descriptive_stat.insert(loc=5, column='median', value=df.median())
descriptive_stat=descriptive_stat.apply(pd.to_numeric, errors='ignore')
descriptive_stat | _____no_output_____ | MIT | 210601 gca data analyses.ipynb | rbnjd/gca_data_analyses |
**Descriptive statistics for categorical data** | for col in list(df[['gender','education level']]):
print('variable:', col)
print(df[col].value_counts(dropna=False).to_string())
print('') | variable: gender
Männlich 18
Weiblich 14
Divers 1
NaN 1
variable: education level
Hochschulabschluss 16
Abitur 8
derzeit noch Schüler\*in 5
derzeit noch Schüler/*in 3
Fachhochschulabschluss 1
NaN 1
| MIT | 210601 gca data analyses.ipynb | rbnjd/gca_data_analyses |
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