Commit e79202df authored by Stoop's avatar Stoop
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Merge branch 'ljschinkelshoek-master-patch-71741' into 'master'

Update 2.1_splitting.ipynb

See merge request !22
parents 5181fb65 b2a5bd50
%% Cell type:markdown id: tags:
# Introduction splitting your data
In this notebook we will introduce splitting your data and fitting a model. We will use the penguins dataset. We shall split the data in different train/test sizes to see the effect on the models performance.
%% Cell type:code id: tags:
``` python
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
def plot_train_result(iterations,train_scores,test_scores,train_loss,test_loss):
fig, (ax1,ax2) = plt.subplots(1,2,figsize=(11, 5))
ax1.plot(iterations,train_scores,label='train')
ax1.plot(iterations,test_scores,label='test')
ax1.set_title('Accuracy')
ax2.plot(iterations,train_loss,label='train')
ax2.plot(iterations,test_loss,label='test')
ax2.set_title('Loss')
ax1.legend()
ax2.legend()
plt.show()
```
%% Cell type:code id: tags:
``` python
#Load data and impute.
data = sns.load_dataset('penguins')
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='most_frequent')# strategy can also be mean or median
data.iloc[:,:] = imputer.fit_transform(data)
```
%% Cell type:markdown id: tags:
In this excersise we will only use the numerical columns. So we define a list of columns and a list for the label:
%% Cell type:code id: tags:
``` python
features = ['bill_length_mm','bill_depth_mm','flipper_length_mm','body_mass_g']
label = ['species']
data[features+label]
```
%%%% Output: execute_result
bill_length_mm bill_depth_mm flipper_length_mm body_mass_g species
0 39.1 18.7 181.0 3750.0 Adelie
1 39.5 17.4 186.0 3800.0 Adelie
2 40.3 18.0 195.0 3250.0 Adelie
3 41.1 17.0 190.0 3800.0 Adelie
4 36.7 19.3 193.0 3450.0 Adelie
.. ... ... ... ... ...
339 41.1 17.0 190.0 3800.0 Gentoo
340 46.8 14.3 215.0 4850.0 Gentoo
341 50.4 15.7 222.0 5750.0 Gentoo
342 45.2 14.8 212.0 5200.0 Gentoo
343 49.9 16.1 213.0 5400.0 Gentoo
[344 rows x 5 columns]
%% Cell type:code id: tags:
``` python
#we will underfit the logistic regression to see its effect. And it will warn us that we are underfitting. To stop sklearn from warning us we tell it to ignore underfitting:
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings(action='ignore', category=ConvergenceWarning)
```
%% Cell type:markdown id: tags:
## Excercise 1
As a first excersie we will classify the Adelie penguins based on the penguins bill length, bill depth, flipper length and body mass. We call X the data and Y the labels (which we need as numpy arrays for this logistic regressor):
%% Cell type:code id: tags:
``` python
X=data[features].values
Y=(data[label]=='Adelie').values
y=(data[label]=='Adelie').values
```
%% Cell type:markdown id: tags:
Read the documentations for sklearn's train_test_split and implement the function to split the data in 33% test and 67% training data: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
%% Cell type:code id: tags:
``` python
from sklearn.model_selection import train_test_split
print('training data shape is:',x_train.shape, 'the training labels:',y_train.shape)
print('test data shape is:',x_test.shape,'the test labels',y_test.shape)
print('training data shape is:',X_train.shape, 'the training labels:',y_train.shape)
print('test data shape is:',X_test.shape,'the test labels',y_test.shape)
```
%% Cell type:markdown id: tags:
For classification the log_loss is the best loss funtcion to use and in this instance we will track our models accuracy to assess the performance.
The code below trains a logistic regression classifier for at most 'i' iterations. By letting the number of iterations increase in the for loop we allow the model more and more training time. Finally we plot the training history to see how the accuracy and loss changed over time.
%% Cell type:code id: tags:
``` python
from sklearn.metrics import accuracy_score,log_loss
number_of_iterations=20
train_scores,test_scores,train_loss,test_loss=[],[],[],[]
iterations=[i for i in range(number_of_iterations)]
for i in iterations:
model = LogisticRegression(max_iter=i)
model = model.fit(x_train,y_train.ravel())
y_train_pred=model.predict(x_train)
y_test_pred=model.predict(x_test)
model = model.fit(X_train,y_train.ravel())
y_train_pred=model.predict(X_train)
y_test_pred=model.predict(X_test)
train_scores.append( accuracy_score(y_train,y_train_pred) )
test_scores.append( accuracy_score(y_test,y_test_pred) )
train_loss.append( log_loss(y_train,y_train_pred) )
test_loss.append( log_loss(y_test,y_test_pred) )
```
%% Cell type:code id: tags:
``` python
plot_train_result(iterations,train_scores,test_scores,train_loss,test_loss)
```
%%%% Output: display_data
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)
%% Cell type:markdown id: tags:
## Exercise 2
By trying out different configurations and plotting the result: Find a good balance between train and test size and find a good number of itereations to let the model train.
What did you learn?
%% Cell type:raw id: tags:
#Your answer here
%% Cell type:markdown id: tags:
## Exercise 3
Now lets try the same but with a regression problem: lets predict the body mass of a penguin based on its bill and flipper length and bill depth.
%% Cell type:code id: tags:
``` python
features = ['bill_length_mm','bill_depth_mm','flipper_length_mm']
label = ['body_mass_g']
X=data[features].values
Y=data[label].values
x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=.3)
y=data[label].values
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.3)
```
%% Cell type:code id: tags:
``` python
from sklearn.metrics import accuracy_score,mean_squared_error
train_scores=[]
test_scores=[]
train_loss=[]
test_loss=[]
iterations=[i for i in range(15)]
for i in iterations:
model = LogisticRegression(max_iter=i)
model = model.fit(x_train,y_train.ravel())
y_train_pred=model.predict(x_train)
y_test_pred=model.predict(x_test)
model = model.fit(X_train,y_train.ravel())
y_train_pred=model.predict(X_train)
y_test_pred=model.predict(X_test)
train_scores.append( accuracy_score(y_train,y_train_pred) )
test_scores.append( accuracy_score(y_test,y_test_pred) )
train_loss.append( mean_squared_error(y_train,y_train_pred) )
test_loss.append( mean_squared_error(y_test,y_test_pred) )
```
%% Cell type:code id: tags:
``` python
plot_train_result(iterations,train_scores,test_scores,train_loss,test_loss)
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
Again: play around with different train/test sizes and number of iterations. <br>
Question: What does the accuracy tell you in this example? (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score)
%% Cell type:raw id: tags:
#Your answer
%% Cell type:markdown id: tags:
Question: how does this problem compare to the classification? What differences do you observe in the training history?
%% Cell type:raw id: tags:
#Your answer
%% Cell type:code id: tags:
``` python
```
......
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