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**[Pandas Home Page](https://www.kaggle.com/learn/pandas)**--- IntroductionIn these exercises we'll apply groupwise analysis to our dataset.Run the code cell below to load the data before running the exercises. | import pandas as pd
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
#pd.set_option("display.max_rows", 5)
from learntools.core import binder; binder.bind(globals())
from learntools.pandas.grouping_and_sorting import *
print("Setup complete.") | _____no_output_____ | MIT | 04 Pandas Certificate/Exercise Grouping and Sorting.ipynb | mateuszczubkowski/introduction-to-python |
Exercises 1.Who are the most common wine reviewers in the dataset? Create a `Series` whose index is the `taster_twitter_handle` category from the dataset, and whose values count how many reviews each person wrote. | # Your code here
reviews_written = reviews.groupby('taster_twitter_handle').size()
# Check your answer
q1.check()
#q1.hint()
#q1.solution() | _____no_output_____ | MIT | 04 Pandas Certificate/Exercise Grouping and Sorting.ipynb | mateuszczubkowski/introduction-to-python |
2.What is the best wine I can buy for a given amount of money? Create a `Series` whose index is wine prices and whose values is the maximum number of points a wine costing that much was given in a review. Sort the values by price, ascending (so that `4.0` dollars is at the top and `3300.0` dollars is at the bottom). | best_rating_per_price = reviews.groupby('price')['points'].max().sort_index()
# Check your answer
q2.check()
#q2.hint()
#q2.solution() | _____no_output_____ | MIT | 04 Pandas Certificate/Exercise Grouping and Sorting.ipynb | mateuszczubkowski/introduction-to-python |
3.What are the minimum and maximum prices for each `variety` of wine? Create a `DataFrame` whose index is the `variety` category from the dataset and whose values are the `min` and `max` values thereof. | price_extremes = reviews.groupby('variety').price.agg([min, max])
# Check your answer
q3.check()
#q3.hint()
#q3.solution() | _____no_output_____ | MIT | 04 Pandas Certificate/Exercise Grouping and Sorting.ipynb | mateuszczubkowski/introduction-to-python |
4.What are the most expensive wine varieties? Create a variable `sorted_varieties` containing a copy of the dataframe from the previous question where varieties are sorted in descending order based on minimum price, then on maximum price (to break ties). | sorted_varieties = reviews.groupby('variety').price.agg([min, max]).sort_values(by=['min', 'max'], ascending=False)
# Check your answer
q4.check()
#q4.hint()
#q4.solution() | _____no_output_____ | MIT | 04 Pandas Certificate/Exercise Grouping and Sorting.ipynb | mateuszczubkowski/introduction-to-python |
5.Create a `Series` whose index is reviewers and whose values is the average review score given out by that reviewer. Hint: you will need the `taster_name` and `points` columns. | reviewer_mean_ratings = reviews.groupby('taster_name').points.mean()
# Check your answer
q5.check()
#q5.hint()
#q5.solution() | _____no_output_____ | MIT | 04 Pandas Certificate/Exercise Grouping and Sorting.ipynb | mateuszczubkowski/introduction-to-python |
Are there significant differences in the average scores assigned by the various reviewers? Run the cell below to use the `describe()` method to see a summary of the range of values. | reviewer_mean_ratings.describe() | _____no_output_____ | MIT | 04 Pandas Certificate/Exercise Grouping and Sorting.ipynb | mateuszczubkowski/introduction-to-python |
6.What combination of countries and varieties are most common? Create a `Series` whose index is a `MultiIndex`of `{country, variety}` pairs. For example, a pinot noir produced in the US should map to `{"US", "Pinot Noir"}`. Sort the values in the `Series` in descending order based on wine count. | country_variety_counts = reviews.groupby(['country', 'variety']).size().sort_values(ascending=False)
# Check your answer
q6.check()
#q6.hint()
#q6.solution() | _____no_output_____ | MIT | 04 Pandas Certificate/Exercise Grouping and Sorting.ipynb | mateuszczubkowski/introduction-to-python |
![donate.jpg](data:image/jpeg;base64,/9j/4AAQSkZJRgABAgEASABIAAD/4Q9lRXhpZgAATU0AKgAAAAgABwESAAMAAAABAAEAAAEaAAUAAAABAAAAYgEbAAUAAAABAAAAagEoAAMAAAABAAIAAAExAAIAAAAcAAAAcgEyAAIAAAAUAAAAjodpAAQAAAABAAAApAAAANAACvyAAAAnEAAK/IAAACcQQWRvYmUgUGhvdG9zaG9wIENTMiBXaW5kb3dzADIwMTg6MDI6MDUgMTQ6MDg6NTkAAAAAA6ABAAMAAAAB//8AAKACAA... | import numpy as np
import pandas as pd
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Importing library for visualization** | import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Importing library for splitting model into train and test and for data transformation** | from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix,accuracy_score,roc_auc_score
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Filter the unwanted warning** | import warnings
warnings.simplefilter("ignore")
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Importing all the required model for model comparision** | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
** Reading the Data** | train = pd.read_csv("blood-train.csv")
test = pd.read_csv("blood-test.csv") | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Printing the train and test size** | print("Train Shape : ",train.shape)
print("Test Shape : ", test.shape)
| Train Shape : (576, 6)
Test Shape : (200, 5)
| Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Printing first five rows of data** | train.head() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Counting the number of people who donated and not donated** | train["Made Donation in March 2007"].value_counts() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Storing dependent variable in Y** | Y = train.iloc[:,-1]
Y.head()
train.tail() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Removing Unnamed: 0 columns** | old_train = train
train = train.iloc[:, 1:5]
test = test.iloc[:, 1:5] | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Printing first rows** | train.head() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Merging both train and test data** | df = pd.merge(train,test)
df.head()
df.describe() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Setting the independent variable and dependent variable** | X = df.iloc[:,:]
X.head() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
** Data Exploration*** Statistics of the data* | train.describe() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Boxplot for Different variables* | plt.figure(figsize=(20,20))
ax = sns.boxplot(x="Months since First Donation", y="Number of Donations", data=old_train)
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
***Boxplot for Months since Last Donation** | plt.figure(figsize=(20,10))
ax = sns.boxplot(y="Months since Last Donation", data=old_train)
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**We see from the above boxplot that the maximum people have donated blood in nearby 10 months.** **Correlation between all variables [Checking how different variable are related]** | corrmat = X.corr()
f, ax = plt.subplots(figsize=(9, 8))
sns.heatmap(corrmat, ax = ax, cmap= "YlGnBu", linewidths= 0.1,fmt= ".2f",annot=True) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Printing all unique value for Month Since Last donation** | train["Months since Last Donation"].unique()
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Feature Engineering** Volume donated is also a good feature to know wether the donor will donate or not. **Creating new variable for calculating how many times a person have donated** | X["Donating for"] = (X["Months since First Donation"] - X["Months since Last Donation"])
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Seeing first five rows of the DataFrame** | X.head() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Correlation between all variables** | corrmat=X.corr()
f, ax = plt.subplots(figsize =(9, 8))
sns.heatmap(corrmat, ax = ax, cmap ="YlGnBu", linewidths = 0.1,fmt = ".2f",annot=True)
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Since Total Volume Donated (c.c.) have the very high correlation with other variables so we are dropping the variable.** **Dropping the unnecessary column** | X.drop([ 'Total Volume Donated (c.c.)'], axis=1, inplace=True)
X.head() | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Shape of independent variable** | X.shape | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
** Feature Transformation** **Feature Scaling** | from sklearn.preprocessing import StandardScaler
scale=StandardScaler()
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Fitting and transforming data** | X=scale.fit_transform(X)
train=X[:576]
train.shape
test=X[576:]
test=X[576:]
Y.shape | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
** Model Building** **Splitting into train and test set** | xtrain,xtest,ytrain,ytest=train_test_split(train,Y,test_size=0.2,random_state=0)
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
StepsTo Follow Create the object Do the necessary hyperparameter tuning Fit the model Predict the test set Compute roc_auc_score Repeat above step for all model Compare roc_auc_Score of all model and choose the best model * Logistic regression*  | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Fitting the model* | logreg.fit(xtrain,ytrain) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Predicting on the test data* | pred = logreg.predict(xtest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*checking accuracy_score* | accuracy_score(pred,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Printing the roc_auc_score* | roc_auc_score(pred,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
** Support Vector Machine**  | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Fitting the model* | SVMC.fit(train,Y) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Predicting on the test data* | pred = SVMC.predict(xtest)
accuracy_score(pred, ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Printing the confusion matrix* | confusion_matrix(pred,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Printing the roc auc score* | roc_auc_score(pred,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
** Random Forest**  | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Fitting the model* | RFC.fit(xtrain,ytrain) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Predicting the test data result* | confusion_matrix(pred,ytest)
accuracy_score(pred, ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*accuracy_score(pred, ytest)* | roc_auc_score(pred,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
** Decision Tree** 
| _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Fitting the model* | model.fit(xtrain,ytrain) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Predicting the test data* | accuracy_score(pred, ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*printing the confusion matrix* | confusion_matrix(pred,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Printing accuracy score* | accuracy_score(pred,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Printing roc auc score* | roc_auc_score(pred,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
** MLP Classifier** ,random_state=1)
clf_neural.fit(train, Y) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Predicting from the fitted model on test data* | print('Predicting...\nIn Test Data')
predicted = clf_neural.predict(xtest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*printing confusion matrix* | confusion_matrix(predicted,ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
*Printing roc auc score* | roc_auc_score(pred,ytest)
accuracy_score(pred, ytest) | _____no_output_____ | Apache-2.0 | Blood_Donation_Analysis.ipynb | rahuldiamond/Blood-Donation-Analysis- |
**Thermodynamics: an Engineering Approach, 7th Ed**Cengel & Boles Chapter 1: Introduction and Basic Concepts Example 1-1, Page.8 | #Diketahui:
El_USD = 0.09 # Harga Listrik adalah 0.09 $/kWh
P_wt = 30 # Wind Turbine power rate, kW
t_wt = 2200 # Durasi kerja Wind Turbine dalam satu tahun, hours
#Dicari: pengiritan per tahun (?)
#Jawab:
E_tot = P_wt * t_wt
print ('Total energi adalah %f kWh' %round(E_tot,0))
#Uang yang diirit:
Saved_USD = E_tot *... | Total energi adalah 66000.000000 kWh
Total pengiritan adalah 5940.000000 USD
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-2, Page No.9 | #Diketahui:
p=850;# densitas [kg/m^3]
V=2; # volume tangki [m^3]
#Dicari: Massa (?)
m=p*V;# rumus massa
#Hasil:
print ('Massa minyak di dalam tangki adalah %i kg' %round(m,0))
| Massa minyak di dalam tangki adalah 1700 kg
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-3, Page No.10 |
#Diketahui
m=1; # massa seberat 1 lbm
#Konstanta yang dipakai
g=32.174;# konstanta gravitasi, ft/s^2
#Dicari: Berat (w)
lbf = 32.174 # Konversi 1 lbf = 32.174 lbm.ft/s^2
w=(m*g)*(1/lbf); # berat = massa * gravitasi
# konversi lbf ke lbm diperlukan
#Result
print ('Berat benda tersebut di bumi adala... | Berat benda tersebut di bumi adalah 1 lbf
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-4, Page No.21 |
# Diketahui
Tc=10; #deltaT karena proses pemberian kalor, C
# Calculations
Tk=Tc;
Tr=1.8*Tk;#Konversi dari R ke K
Tf=Tr;
# Dihitung dengan rumus di atas
#Hasil
print ('perubahan suhu tersebut adalah %i K' %Tk)
print ('perubahan suhu tersebut adalah %i R' %Tr)
print ('perubahan suhu tersebut adalah %i F' %Tf)
| perubahan suhu tersebut adalah 10 K
perubahan suhu tersebut adalah 18 R
perubahan suhu tersebut adalah 18 F
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-5, Page No.23 | #Diketahui
Patm=14.5; #tekanan atmosfir, psi
Pvac=5.8; #pembacaan vacuum gage, psi
#Proses perhitungan
Pabs=Patm-Pvac;#vacuum selalu dihitung sbg tekanan negatif
#Hasil
print('P_absolut dari ruangan tersebut adalah %f psi'%round(Pabs,1))
| P_absolut dari ruangan tersebut adalah 8.700000 psi
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-6, Page No.26 | #Diketahui:
pw=1000; # Densitas air, kg/m^3;
g=9.81; # Gravitasi, m/s^2;
SG=0.85;# Specific Gravity/Dens. Relatif fluida di manometer
meter = 100 # 1 m = 100 cm, cm
h=55/meter;# tinggi dalam satuan, cm
Patm=96;# Tekanan Atmosfir, kPa
# Jawab
# Menghitung P menggunakan likuid pada ketinggian yang sama
p=SG*pw;
Ptank_ab... | absolute pressure in tank 100.600000 kPa
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-7, Page No.28 | #Constants used
g=9.81;#acceleration due to gravity in m/s^2;
#Given values
h1=0.1;# distance b/w point 1 at air-water interface and point 2 at mercury-air interface in m
h2=0.2;# distance b/w oil-water interface and mercury-oil interface in m
h3=0.35;# distance b/w air-mercury interface and mercury-oil interface in... | the air pressure in tank 130 kPa
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-8, Page No.31 | #Constants used
g=9.81;# acceleration due to gravity in m/s^2;
#Given values
pHg=13570;# density of mercury at 10 C in kg/m^3
h=0.74;# converting barometric reading into m from mm
#Calculationa
Patm=pHg*g*h/1000;# standard pressure formula
#Results
print ('the atmospheric pressure %f kPa' %round(Patm,1))
| the atmospheric pressure 98.500000 kPa
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-9, Page No.31 | #constants used
g=9.81;#acceleration due to gravity in m/s^2;
#given values
m=60;# mass of piston in kg
Patm=0.97;# atmospheric pressure in kPa
A=0.04;# cross-sectional area in m^2
#calculation
P=Patm+(m*g/A)/100000;# standard pressure formula
print ('The pressure inside the cylinder %f bar' %round(P,2))
#The volume... | The pressure inside the cylinder 1.120000 bar
If some heat is transferred to the gas and its volume is doubled, there is no change in pressure
| MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Example 1-10, Page No.32 | import math
from scipy.integrate import quad
from pylab import *
#Constants used
g=9.81;#acceleration due to gravity in m/s^2;
#Given values
p=1040;# density on the water surface in kg/m^3
h1=0.8;# thickness of surface zone
H=4;# thickness of gradient zone
x0=0.0;# lower limit of integration
x1=4.0;# upper limit of ... | _____no_output_____ | MIT | Thermodynamics_an Eng Approach_5Ed_Cengel_IDN/.ipynb_checkpoints/Chapter_1_1-checkpoint.ipynb | msuherma/termodinamika |
Assignment 1 We are given a 2-dimensional grid with points $(i, j)$, $i, j = 0, \dots, N+1$. In this assignment we want to simulate a discrete diffusion process on the grid. We are starting with a distribution $u_0(i, j)$ of function values on the grid points. The distribution process follows the following recurrence ... | def diffusion_iteration(un):
"""
Perform one diffusion step for all given grid points.
Parameters
----------
un : numpy.ndarray
Numpy array of type `float64` and dimension (N + 2, N + 2) that stores the
function values at step n.
This function returns a Numpy array ... | _____no_output_____ | BSD-3-Clause | hpc_lecture_notes/assignment_1.ipynb | skailasa/hpc_lecture_notes |
Ejercicio: Spectral clustering para documentosEl clustering espectral es una técnica de agrupamiento basada en la topología de gráficas. Es especialmente útil cuando los datos no son convexos o cuando se trabaja, directamente, con estructuras de grafos.Preparación d elos documentosTrabajaremos con documentos textuales... | #Se importan las librerías necesarias
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from gensim.models.doc2vec import Doc2Vec, TaggedDocument | _____no_output_____ | MIT | notebooks/04_Ejercicio_spectral_clust.ipynb | medinaalonso/NLP |
La librería de Natural Language Toolkit (nltk) proporciona algunos corpus con los que se puede trabajar. Por ejemplo, el cropus Gutenberg (https://web.eecs.umich.edu/~lahiri/gutenberg_dataset.html) del que usaremos algunos datos. Asimismo, obtendremos de esta librería herramientas de preprocesamiento: stemmer y lista d... | import nltk
#Descarga del corpus
nltk.download('gutenberg')
#Descarga de la lista de stopwords
nltk.download('stopwords')
from nltk.corpus import gutenberg
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer | [nltk_data] Downloading package gutenberg to /root/nltk_data...
[nltk_data] Unzipping corpora/gutenberg.zip.
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data] Unzipping corpora/stopwords.zip.
| MIT | notebooks/04_Ejercicio_spectral_clust.ipynb | medinaalonso/NLP |
Definimos los nombres de los archivos (ids) y la lista de paro | #Obtiene ids de los archivos del corpus gutenberg
doc_labels = gutenberg.fileids()
#Lista de stopwords para inglés
lista_paro = stopwords.words('english') | _____no_output_____ | MIT | notebooks/04_Ejercicio_spectral_clust.ipynb | medinaalonso/NLP |
Definiremos una función que se encargará de preprocesar los textos. Se eliminan símbolos, se quitan elementos de la lista de stopwords y se pasa todo a minúsculas. | def preprocess(document):
#Lista que guarda archivos limpios
text = []
for word in document:
#Minúsculas
word = word.lower()
#Elimina stopwords y símbolos
if word not in lista_paro and word.isalpha() == True:
#Se aplica stemming
text.append(PorterStemmer().stem(word))
return text | _____no_output_____ | MIT | notebooks/04_Ejercicio_spectral_clust.ipynb | medinaalonso/NLP |
Por cada documento, obtenemos la lista de sus palabras (stems) aplicando un preprocesado. Cada documento, entonces, es de la forma $d_i = \{w_1, w_2, ..., w_{N_i}\}$, donde $w_k$ son los stems del documento. | docs = []
for doc in doc_labels:
#Lista de palabras del documentos
arx = gutenberg.words(doc)
#Aplica la función de preprocesado
arx_prep = preprocess(arx)
docs.append(arx_prep)
#Imprime el nombre del documento, su longitud original y su longitud con preproceso
print(doc,len(arx), len(arx_prep)) | austen-emma.txt 192427 73149
austen-persuasion.txt 98171 38337
austen-sense.txt 141576 53986
bible-kjv.txt 1010654 374945
blake-poems.txt 8354 3805
bryant-stories.txt 55563 21718
burgess-busterbrown.txt 18963 7582
carroll-alice.txt 34110 12240
chesterton-ball.txt 96996 39715
chesterton-brown.txt 86063 35348
chesterton-... | MIT | notebooks/04_Ejercicio_spectral_clust.ipynb | medinaalonso/NLP |
Posteriormente, convertiremos cada documento en un vector en $\mathbb{R}^d$. Para esto, utilizaremos el algoritmo Doc2Vec. | #Dimensión de los vectores
dim = 300
#tamaño de la ventana de contexto
windows_siz = 15
#Indexa los documentos con valores enteros
documents = [TaggedDocument(doc_i, [i]) for i, doc_i in enumerate(docs)]
#Aplica el modelo de Doc2Vec
model = Doc2Vec(documents, vector_size=dim, window=windows_siz, min_count=1)
#Matriz ... | [[-0.71790749 0.52590156 0.04665314 ... -1.11765587 0.27269235
0.17521498]
[-0.99053538 0.81888688 0.08494744 ... -1.4580884 0.35552233
0.36190808]
[-1.1343857 0.89900047 0.07679608 ... -1.67795575 0.4307873
0.40497407]
...
[-1.57137489 1.01522303 0.13583013 ... -2.54420257 0.6712212
0.232... | MIT | notebooks/04_Ejercicio_spectral_clust.ipynb | medinaalonso/NLP |
Visualización | #Función para plotear
def plot_words(Z,ids,color='blue'):
#Reduce a dos dimensiones con PCA
Z = PCA(n_components=2).fit_transform(Z)
r=0
#Plotea las dimensiones
plt.scatter(Z[:,0],Z[:,1], marker='o', c=color)
for label,x,y in zip(ids, Z[:,0], Z[:,1]):
#Agrega las etiquetas
plt.an... | _____no_output_____ | MIT | notebooks/04_Ejercicio_spectral_clust.ipynb | medinaalonso/NLP |
Task 2: Image Enhancement II: Spatial SmoothingIn this task, we will implement average, gaussian, and median spatial filter. | %%bash
pip install git+https://github.com/ee604/ee604_plugins
# Importing required libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt
from ee604_plugins import download_dataset, cv2_imshow
download_dataset(assignment_no=2, task_no=2) # download data for this assignment
def avgFilter(img, kernel_s... | _____no_output_____ | MIT | assignment_2/Task_2.ipynb | antranx/ee604_assignments |
Test---Your observation should compare the different methods for different images. Must include a sentence on which method + kernel size worked best in each case. | # Do not change codes inside this cell
# Add your observations in next to next cell
# Your observation should compare the different methods for different images
lena_orig = cv2.imread('data/lena_gray.jpg', 0)
lena_noisy_1 = cv2.imread('data/lena_noisy_1.jpg', 0)
lena_noisy_2 = cv2.imread('data/lena_noisy_2.jpg', 0)
le... | _____no_output_____ | MIT | assignment_2/Task_2.ipynb | antranx/ee604_assignments |
Machine Learning Model Building Pipeline: Wrapping up for DeploymentIn the previous lectures, we worked through the typical Machine Learning pipeline to build a regression model that allows us to predict house prices. Briefly, we transformed variables in the dataset to make them suitable for use in a Regression model,... | # to handle datasets
import pandas as pd
import numpy as np
# to divide train and test set
from sklearn.model_selection import train_test_split
# feature scaling
from sklearn.preprocessing import MinMaxScaler
# to build the models
from sklearn.linear_model import Lasso
# to evaluate the models
from sklearn.metrics ... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Load dataWe need the training data to train our model in the production environment. | data_folder = '/Users/michaelpadilla/projects/udemy_deployment/data/house-prices-advanced-regression-techniques/'
# load dataset
data = pd.read_csv(os.path.join(data_folder, 'houseprice.csv'))
print(data.shape)
data.head() | (1460, 81)
| BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Separate dataset into train and testBefore beginning to engineer our features, it is important to separate our data intro training and testing set. This is to avoid over-fitting. There is an element of randomness in dividing the dataset, so remember to set the seed. | # Let's separate into train and test set
# Remember to seet the seed (random_state for this sklearn function)
X_train, X_test, y_train, y_test = train_test_split(data, data.SalePrice,
test_size=0.1,
random_state=0) ... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Selected featuresRemember that we will deploy our model utilising only a subset of features, the most predictive ones. This is to make simpler models, so that we build simpler code for deployment. We will tell you more about this in coming lectures. | # load selected features
features = pd.read_csv(os.path.join(data_folder, 'selected_features.csv'), header=None)
# Remember that I added the extra feature, to show you how to put
# an additional feature engineering step into production
features = [x for x in features[0]] + ['LotFrontage']
print('Number of features: ',... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Missing valuesFor categorical variables, we will fill missing information by adding an additional category: "missing" | # make a list of the categorical variables that contain missing values
vars_with_na = [var for var in features if X_train[var].isnull().sum()>1 and X_train[var].dtypes=='O']
# print the variable name and the percentage of missing values
for var in vars_with_na:
print(var, np.round(X_train[var].isnull().mean()*100,... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Note that we have much less categorical variables with missing values than in our original dataset. But we still use categorical variables with NA for the final model, so we need to include this piece of feature engineering logic in the deployment pipeline. | # I bring forward the functions used in the feature engineering notebook:
# function to replace NA in categorical variables
def fill_categorical_na(df, var_list):
X = df.copy()
X[var_list] = df[var_list].fillna('Missing')
return X
# replace missing values with new label: "Missing"
X_train = fill_categoric... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
For numerical variables, we are going to add an additional variable capturing the missing information, and then replace the missing information in the original variable by the mode, or most frequent value: | # make a list of the numerical variables that contain missing values
vars_with_na = [var for var in features if X_train[var].isnull().sum()>1 and X_train[var].dtypes!='O']
# print the variable name and the percentage of missing values
for var in vars_with_na:
print(var, np.round(X_train[var].isnull().mean()*100, 2... | LotFrontage 17.73 % missing values
| BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Important: persisting the mean value for NA imputationAs you will see in future sections, one of the key pieces of deploying the model is "Model Validation". Model validation refers to corroborating that the deployed model and the model built during research, are identical. The entire pipeline needs to produce identic... | X_train['LotFrontage'].mode()[0]
# replace the missing values
mean_var_dict = {}
for var in vars_with_na:
# calculate the mode
mode_val = X_train[var].mode()[0]
# we persist the mean in the dictionary
mean_var_dict[var] = mode_val
# train
# note that the additional binary varia... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Temporal variablesOne of our temporal variables was selected to be used in the final model: 'YearRemodAdd'So we need to deploy the bit of code that creates it. | # create the temporal var "elapsed years"
def elapsed_years(df, var):
# capture difference between year variable and year the house was sold
df[var] = df['YrSold'] - df[var]
return df
X_train = elapsed_years(X_train, 'YearRemodAdd')
X_test = elapsed_years(X_test, 'YearRemodAdd')
for var in ['LotFrontage', '... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Categorical variablesWe do have categorical variables in our final model. First, we will remove those categories within variables that are present in less than 1% of the observations: | # let's capture the categorical variables first
cat_vars = [var for var in features if X_train[var].dtype == 'O']
cat_vars | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Important: persisting the frequent labelsAs you will see in future sections, one of the key pieces of deploying the model is "Model Validation". Model validation refers to corroborating that the deployed model and the model built during research, are identical. The entire pipeline needs to produce identical results.Th... | tmp = X_train.groupby(cat_vars[0])['SalePrice'].count() / len(X_train)
tmp > 0.01
def find_frequent_labels(df, var, rare_perc):
# finds the labels that are shared by more than a certain % of the houses in the dataset
df = df.copy()
tmp = df.groupby(var)['SalePrice'].count() / len(df)
return tmp[tmp>rare... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Next, we need to transform the strings of these variables into numbers. We will do it so that we capture the monotonic relationship between the label and the target: | # this function will assign discrete values to the strings of the variables,
# so that the smaller value corresponds to the smaller mean of target
def replace_categories(train, test, var, target):
train = train.copy()
test = test.copy()
ordered_labels = train.groupby([var])[target].mean().sort_values... | _____no_output_____ | BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Feature ScalingFor use in linear models, features need to be either scaled or normalised. In the next section, I will scale features between the min and max values: | # capture the target
y_train = X_train['SalePrice']
y_test = X_test['SalePrice']
# fit scaler
scaler = MinMaxScaler() # create an instance
scaler.fit(X_train[features]) # fit the scaler to the train set for later use
# we persist the model for future use
joblib.dump(scaler, os.path.join(data_folder, 'scaler.pkl'))
#... | linear train mse: 1087435415.441452
linear train rmse: 32976.28565259362
linear test mse: 1405259552.2596023
linear test rmse: 37486.79170400692
Average house price: 163000.00000000012
| BSD-3-Clause | jupyter_notebooks/Section2_MLPipelineOverview/02.10_ML_Pipeline-WrapUp_for_Deployment.ipynb | mtpadilla/deploying-machine-learning-models |
Copyright (c) Microsoft Corporation. All rights reserved.Licensed under the MIT License. ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png... | import logging
from matplotlib import pyplot as plt
import pandas as pd
import os
import azureml.core
from azureml.core.experiment import Experiment
from azureml.core.workspace import Workspace
from azureml.core.dataset import Dataset
from azureml.train.automl import AutoMLConfig | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb | tomasro27/MachineLearningNotebooks |
This sample notebook may use features that are not available in previous versions of the Azure ML SDK. | print("This notebook was created using version 1.22.0 of the Azure ML SDK")
print("You are currently using version", azureml.core.VERSION, "of the Azure ML SDK")
ws = Workspace.from_config()
# choose a name for experiment
experiment_name = 'automl-classification-ccard-remote'
experiment=Experiment(ws, experiment_name... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb | tomasro27/MachineLearningNotebooks |
Create or Attach existing AmlComputeA compute target is required to execute the Automated ML run. In this tutorial, you create AmlCompute as your training compute resource. Creation of AmlCompute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace this code will skip the creati... | from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
# Choose a name for your CPU cluster
cpu_cluster_name = "cpu-cluster-1"
# Verify that cluster does not exist already
try:
compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)
... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb | tomasro27/MachineLearningNotebooks |
Data Load DataLoad the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model. | data = "https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv"
dataset = Dataset.Tabular.from_delimited_files(data)
training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)
label_column_name = 'Class' | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb | tomasro27/MachineLearningNotebooks |
TrainInstantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.|Property|Description||-|-||**task**|classification or regression||**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: accuracyAUC_weightedaverage_pr... | automl_settings = {
"n_cross_validations": 3,
"primary_metric": 'average_precision_score_weighted',
"enable_early_stopping": True,
"max_concurrent_iterations": 2, # This is a limit for testing purpose, please increase it as per cluster size
"experiment_timeout_hours": 0.25, # This is a time limit fo... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb | tomasro27/MachineLearningNotebooks |
Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous. | remote_run = experiment.submit(automl_config, show_output = False)
# If you need to retrieve a run that already started, use the following code
#from azureml.train.automl.run import AutoMLRun
#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')
remote_run | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb | tomasro27/MachineLearningNotebooks |
Results Widget for Monitoring RunsThe widget will first report a "loading" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.**Note:** The wi... | from azureml.widgets import RunDetails
RunDetails(remote_run).show()
remote_run.wait_for_completion(show_output=False) | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb | tomasro27/MachineLearningNotebooks |
Explain modelAutomated ML models can be explained and visualized using the SDK Explainability library. Analyze results Retrieve the Best ModelBelow we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the ... | best_run, fitted_model = remote_run.get_output()
fitted_model | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb | tomasro27/MachineLearningNotebooks |
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