headers

DDE_I_ML_Dynamical_Systems/Lecture 10/Lecture_8.ipynb

 %% Cell type:markdown id: tags:
 
-#Active Session 8: Autoencoders
+# Active Session 8: Autoencoders
 
 %% Cell type:markdown id: tags:
 
-#Important Note
+# Important Note
 
 Lecture notes and notebooks must not be copied and/or distributed without the express permission of ITS.
 
 
 
 
 %% Cell type:markdown id: tags:
 
-#1. Problem Definition: Probe into the Data
+# 1. Problem Definition: Probe into the Data
 
 In this dataset, we will be looking into manufacturing error analysis. The dataset includes modified sensory inputs collected on the manufacturing line and we will be using this multi-dimensional input data to predict the defective products via dimensionality reduction.
 
 Note that the data set includes over 280,000 instances, where only a small fraction (~500) is defective.
 
 %% Cell type:markdown id: tags:
 
 
 # 2. Preparing the enviroment
 
 Import the Python libraries that we will need to (i) load the data, (ii) analyze it, (iii) create our model, (iv) process the results.
 
 %% Cell type:code id: tags:
 
 ``` 
 !pip install ipython-autotime
 %load_ext autotime
 ```
 
 %%%% Output: stream
 
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     time: 1.37 ms (started: 2021-01-18 16:03:04 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 #Importing the necessary libraries
 import numpy as np
 import pandas as pd
 import os
 import io
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.cm as cm
 import seaborn as sns
 color = sns.color_palette()
 import matplotlib as mpl
 #import altair as alt
 import plotly.express as px
 ```
 
 %%%% Output: stream
 
     time: 586 ms (started: 2021-01-18 16:03:04 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 # Data Preparation
 from sklearn import preprocessing as pp
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import precision_recall_curve, average_precision_score
 from sklearn.metrics import confusion_matrix, classification_report
 ```
 
 %%%% Output: stream
 
     time: 39 ms (started: 2021-01-18 16:03:05 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 # ML Algorithms to be used
 import tensorflow as tf
 import keras
 from keras import backend as K
 from keras.models import Sequential, Model
 from keras import optimizers, models, layers, regularizers
 from keras.layers import Activation, Dense, Dropout
 from keras.layers import BatchNormalization, Input, Lambda
 from keras.losses import mse, binary_crossentropy
 ```
 
 %%%% Output: stream
 
     time: 1.42 s (started: 2021-01-18 16:03:05 +00:00)
 
 %% Cell type:markdown id: tags:
 
 # 3. Pre-processing
 
 %% Cell type:markdown id: tags:
 
 ## Loading the Data
 
 We need to upload the dataset to Colab enviroment. Pandas library is a practical way to load and read the data from an url.
 
 The data is on ILIAS so this week you need to upload the data from your local pc / by using Google Drive link.
 
 %% Cell type:code id: tags:
 
 ``` 
 # Loading the data
 
 #Local drive:
 #from google.colab import files
 #uploaded = files.upload()
 #data = pd.read_csv('manufacturing.csv')
 #data.head()
 ```
 
 %%%% Output: stream
 
     time: 1.49 ms (started: 2021-01-18 16:03:06 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 # Loading the data
 !pip install -U -q PyDrive
 from pydrive.auth import GoogleAuth
 from pydrive.drive import GoogleDrive
 from google.colab import auth
 from oauth2client.client import GoogleCredentials# Authenticate and create the PyDrive client.
 auth.authenticate_user()
 gauth = GoogleAuth()
 gauth.credentials = GoogleCredentials.get_application_default()
 drive = GoogleDrive(gauth)
 ```
 
 %%%% Output: stream
 
     time: 2.22 s (started: 2021-01-18 16:03:06 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 downloaded = drive.CreateFile({'id':'1mQZTB5gEkal_qH0TiivXAJY082Wclfbo'})
 downloaded.GetContentFile('manufacturing.csv')
 data = pd.read_csv('manufacturing.csv')
 data.head()
 ```
 
 %%%% Output: execute_result
 
        Time        S1        S2        S3  ...       S26       S27       S28    Class
     0   0.0 -1.359807 -0.072781  2.536347  ... -0.189115  0.133558 -0.021053  regular
     1   0.0  1.191857  0.266151  0.166480  ...  0.125895 -0.008983  0.014724  regular
     2   1.0 -1.358354 -1.340163  1.773209  ... -0.139097 -0.055353 -0.059752  regular
     3   1.0 -0.966272 -0.185226  1.792993  ... -0.221929  0.062723  0.061458  regular
     4   2.0 -1.158233  0.877737  1.548718  ...  0.502292  0.219422  0.215153  regular
     
     [5 rows x 30 columns]
 
 %%%% Output: stream
 
     time: 9.51 s (started: 2021-01-18 16:03:08 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 os.listdir('.')
 ```
 
 %%%% Output: execute_result
 
     ['.config',
      'my_best_model_AE_2.h5',
      'my_best_model_AE_reLU.h5',
      'manufacturing.csv',
      'my_best_model_AE_int.h5',
      'adc.json',
      'my_best_model_AE.h5',
      'my_best_model_noise.h5',
      'my_best_model_AE_Sparse.h5',
      'my_best_model_AE_Sparse_2.h5',
      'sample_data']
 
 %%%% Output: stream
 
     time: 3.48 ms (started: 2021-01-18 16:03:18 +00:00)
 
 %% Cell type:markdown id: tags:
 
 ## Data Exploration
 Here we will look into the statistics of the data, identify any missing values or categorical features that is needed to be further process.
 
 Let’s analyze our dataset first. Use dataset.head(n) to display top n data. You can change dataset.head(n) to dataset.sample(n) to display randomly picked data:
 
 %% Cell type:code id: tags:
 
 ``` 
 data.sample(5)
 ```
 
 %%%% Output: execute_result
 
                Time        S1        S2  ...       S27       S28    Class
     132851  80143.0 -2.635638  2.371788  ... -0.250755 -0.006164  regular
     118123  74958.0 -1.060513  0.080976  ... -0.075486  0.053509  regular
     132847  80141.0 -1.347806  0.513449  ... -0.370661 -0.022077  regular
     48752   43773.0  1.015943 -0.013354  ...  0.025135  0.015417  regular
     17983   29087.0  1.356317 -0.392223  ... -0.030075 -0.002300  regular
     
     [5 rows x 30 columns]
 
 %%%% Output: stream
 
     time: 54.6 ms (started: 2021-01-18 16:03:18 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 data.info()
 ```
 
 %%%% Output: stream
 
     <class 'pandas.core.frame.DataFrame'>
     RangeIndex: 284807 entries, 0 to 284806
     Data columns (total 30 columns):
      #   Column  Non-Null Count   Dtype
     ---  ------  --------------   -----
      0   Time    284807 non-null  float64
      1   S1      284807 non-null  float64
      2   S2      284807 non-null  float64
      3   S3      284807 non-null  float64
      4   S4      284807 non-null  float64
      5   S5      284807 non-null  float64
      6   S6      284807 non-null  float64
      7   S7      284807 non-null  float64
      8   S8      284807 non-null  float64
      9   S9      284807 non-null  float64
      10  S10     284807 non-null  float64
      11  S11     284807 non-null  float64
      12  S12     284807 non-null  float64
      13  S13     284807 non-null  float64
      14  S14     284807 non-null  float64
      15  S15     284807 non-null  float64
      16  S16     284807 non-null  float64
      17  S17     284807 non-null  float64
      18  S18     284807 non-null  float64
      19  S19     284807 non-null  float64
      20  S20     284807 non-null  float64
      21  S21     284807 non-null  float64
      22  S22     284807 non-null  float64
      23  S23     284807 non-null  float64
      24  S24     284807 non-null  float64
      25  S25     284807 non-null  float64
      26  S26     284807 non-null  float64
      27  S27     284807 non-null  float64
      28  S28     284807 non-null  float64
      29  Class   284807 non-null  object
     dtypes: float64(29), object(1)
     memory usage: 65.2+ MB
     time: 45 ms (started: 2021-01-18 16:03:18 +00:00)
 
 %% Cell type:markdown id: tags:
 
 ##Label Encoding
 
 Unlike the previous airfoil example, here we have an object column for the labels (see Class). Lets convert the NaN data into categorical values. Here you can use either pandas dataframe directly or use scikit learn. For scikit implementation, you may check:
 
 https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
 
 For our case, we will directly work on the dataframe and customize the labels as we want:
 
 %% Cell type:code id: tags:
 
 ``` 
 data["Class"].value_counts()
 ```
 
 %%%% Output: execute_result
 
     regular      284315
     defective       492
     Name: Class, dtype: int64
 
 %%%% Output: stream
 
     time: 31.7 ms (started: 2021-01-18 16:03:18 +00:00)
 
 %% Cell type:markdown id: tags:
 
 As you can see, we have two categories; `regular` and `defective`. We want to label them in a way that regulars are '0' and defectives are as '1'. In this case, we can use the `str` accessor plus `np.where` to modify the target column:
 
 %% Cell type:code id: tags:
 
 ``` 
 data["Class"] = np.where(data["Class"].str.contains("reg"), 0, 1)
 data["Class"].value_counts()
 ```
 
 %%%% Output: execute_result
 
     0    284315
     1       492
     Name: Class, dtype: int64
 
 %%%% Output: stream
 
     time: 115 ms (started: 2021-01-18 16:03:18 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 data.info()
 ```
 
 %%%% Output: stream
 
     <class 'pandas.core.frame.DataFrame'>
     RangeIndex: 284807 entries, 0 to 284806
     Data columns (total 30 columns):
      #   Column  Non-Null Count   Dtype
     ---  ------  --------------   -----
      0   Time    284807 non-null  float64
      1   S1      284807 non-null  float64
      2   S2      284807 non-null  float64
      3   S3      284807 non-null  float64
      4   S4      284807 non-null  float64
      5   S5      284807 non-null  float64
      6   S6      284807 non-null  float64
      7   S7      284807 non-null  float64
      8   S8      284807 non-null  float64
      9   S9      284807 non-null  float64
      10  S10     284807 non-null  float64
      11  S11     284807 non-null  float64
      12  S12     284807 non-null  float64
      13  S13     284807 non-null  float64
      14  S14     284807 non-null  float64
      15  S15     284807 non-null  float64
      16  S16     284807 non-null  float64
      17  S17     284807 non-null  float64
      18  S18     284807 non-null  float64
      19  S19     284807 non-null  float64
      20  S20     284807 non-null  float64
      21  S21     284807 non-null  float64
      22  S22     284807 non-null  float64
      23  S23     284807 non-null  float64
      24  S24     284807 non-null  float64
      25  S25     284807 non-null  float64
      26  S26     284807 non-null  float64
      27  S27     284807 non-null  float64
      28  S28     284807 non-null  float64
      29  Class   284807 non-null  int64
     dtypes: float64(29), int64(1)
     memory usage: 65.2 MB
     time: 30.8 ms (started: 2021-01-18 16:03:18 +00:00)
 
 %% Cell type:markdown id: tags:
 
 Let's look into the statistics of the data. This is usually a good starting point to have an idea about the range of the data, its nature, as well as the
 missing information for different features:
 
 %% Cell type:code id: tags:
 
 ``` 
 data.describe()
 ```
 
 %%%% Output: execute_result
 
                     Time            S1  ...           S28          Class
     count  284807.000000  2.848070e+05  ...  2.848070e+05  284807.000000
     mean    94813.859575  1.758743e-12  ... -3.518886e-12       0.001727
     std     47488.145955  1.958696e+00  ...  3.300833e-01       0.041527
     min         0.000000 -5.640751e+01  ... -1.543008e+01       0.000000
     25%     54201.500000 -9.203734e-01  ... -5.295979e-02       0.000000
     50%     84692.000000  1.810880e-02  ...  1.124383e-02       0.000000
     75%    139320.500000  1.315642e+00  ...  7.827995e-02       0.000000
     max    172792.000000  2.454930e+00  ...  3.384781e+01       1.000000
     
     [8 rows x 30 columns]
 
 %%%% Output: stream
 
     time: 381 ms (started: 2021-01-18 16:03:18 +00:00)
 
 %% Cell type:markdown id: tags:
 
 If not defined by the user, you can also explore the features with the following command:
 
 %% Cell type:code id: tags:
 
 ``` 
 data.columns
 ```
 
 %%%% Output: execute_result
 
     Index(['Time', 'S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9', 'S10',
            'S11', 'S12', 'S13', 'S14', 'S15', 'S16', 'S17', 'S18', 'S19', 'S20',
            'S21', 'S22', 'S23', 'S24', 'S25', 'S26', 'S27', 'S28', 'Class'],
           dtype='object')
 
 %%%% Output: stream
 
     time: 4.93 ms (started: 2021-01-18 16:03:19 +00:00)
 
 %% Cell type:markdown id: tags:
 
 It is also possible to explore individual features:
 
 %% Cell type:code id: tags:
 
 ``` 
 data['S12'].median()
 ```
 
 %%%% Output: execute_result
 
     0.140032588
 
 %%%% Output: stream
 
     time: 7.77 ms (started: 2021-01-18 16:03:19 +00:00)
 
 %% Cell type:code id: tags:
 
 ``` 
 data['S12'].mean()
 ```
 
 %%%% Output: execute_result
 
     1.0534877568289322e-12
 
 %%%% Output: stream
 
     time: 2.81 ms (started: 2021-01-18 16:03:19 +00:00)
 
 %% Cell type:markdown id: tags:
 
 ## Identify non-numerical values
 Some ML algorithms can not handle non-numerical values (NaN: not a number) so you may need to identify the type of the data for each feature and modify it if necessary. It is also quite common that different feature values are missing for different instances / examples so you may need to decide what to do: (i) omit the instance; (ii) replace them with the mean / median / mode of the feature; (iv) substitute them with a value of your choice.
 
 The following line counts the NaNs for each feature for us. Note that in previous exercises, we used np for the task; not pandas. Since here we have an integer type in the Dataframe, np.isnan does not work here. This function takes a scalar or array-like object and indicates whether values are missing (NaN in numeric arrays, None or NaN in object arrays, NaT in datetimelike).
 
 %% Cell type:code id: tags:
 
 ``` 
 nanCounter = pd.isnull(data).sum()
 print(nanCounter)
 ```
 
 %%%% Output: stream
 
     Time     0
     S1       0
     S2       0
     S3       0
     S4       0
     S5       0
     S6       0
     S7       0
     S8       0
     S9       0
     S10      0
     S11      0
     S12      0
     S13      0
     S14      0
     S15      0
     S16      0
     S17      0
     S18      0
     S19      0
     S20      0
     S21      0
     S22      0
     S23      0
     S24      0
     S25      0
     S26      0
     S27      0
     S28      0
     Class    0
     dtype: int64
     time: 29.6 ms (started: 2021-01-18 16:03:19 +00:00)
 
 %% Cell type:markdown id: tags:
 
 It is also a good exercise to check the uniqueness of the dataset, that is, whether there exists values repeating at different instances:
 
 %% Cell type:code id: tags:
 
 ``` 
 distinctCounter = data.apply(lambda x: len(x.unique()))
 print(distinctCounter)
 ```
 
 %%%% Output: stream
 
     Time     124592
     S1       275653
     S2       275655
     S3       275657
     S4       275654
     S5       275657
     S6       275652
     S7       275651
     S8       275643
     S9       275656
     S10      275646
     S11      275648
     S12      275654
     S13      275657
     S14      275653
     S15      275653
     S16      275645
     S17      275646
     S18      275655
     S19      275645
     S20      275632
     S21      275617
     S22      275644
     S23      275611
     S24      275645
     S25      275640
     S26      275647
     S27      275597
     S28      275558
     Class         2
     dtype: int64
     time: 216 ms (started: 2021-01-18 16:03:19 +00:00)
 
 %% Cell type:markdown id: tags:
 
 ## Data Visualization
 
 Another important pre-processing step is the data visualization. Histograms are suitable for a holistic view, where we can probe into the data for each attribute.
 
 We can use `hist` from matplotlib for that purpose:
 
 %% Cell type:code id: tags:
 
 ``` 
 data.hist(bins=50, figsize=(25,15))
 plt.show()
 ```
 
 %%%% Output: display_data
 
     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)
 
 %%%% Output: stream
 
     time: 5.3 s (started: 2021-01-18 16:03:19 +00:00)
 
 %% Cell type:markdown id: tags: