From 22b05b3422df5b65e8cbdd6590ab6190c2057f83 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Thu, 17 Oct 2019 14:16:25 +0200 Subject: [PATCH] update on credit card data --- doc/pub/LogReg/html/._LogReg-bs000.html | 6 +- doc/pub/LogReg/html/._LogReg-bs001.html | 4 +- doc/pub/LogReg/html/._LogReg-bs002.html | 4 +- doc/pub/LogReg/html/._LogReg-bs003.html | 4 +- doc/pub/LogReg/html/._LogReg-bs004.html | 4 +- doc/pub/LogReg/html/._LogReg-bs005.html | 4 +- doc/pub/LogReg/html/._LogReg-bs006.html | 4 +- doc/pub/LogReg/html/._LogReg-bs007.html | 4 +- doc/pub/LogReg/html/._LogReg-bs008.html | 4 +- doc/pub/LogReg/html/._LogReg-bs009.html | 4 +- doc/pub/LogReg/html/._LogReg-bs010.html | 4 +- doc/pub/LogReg/html/._LogReg-bs011.html | 4 +- doc/pub/LogReg/html/._LogReg-bs012.html | 4 +- doc/pub/LogReg/html/._LogReg-bs013.html | 4 +- doc/pub/LogReg/html/._LogReg-bs014.html | 4 +- doc/pub/LogReg/html/._LogReg-bs015.html | 4 +- doc/pub/LogReg/html/._LogReg-bs016.html | 4 +- doc/pub/LogReg/html/._LogReg-bs017.html | 212 +------------------ doc/pub/LogReg/html/._LogReg-bs018.html | 102 ++++++++- doc/pub/LogReg/html/LogReg-bs.html | 6 +- doc/pub/LogReg/html/LogReg-reveal.html | 135 ++---------- doc/pub/LogReg/html/LogReg-solarized.html | 134 ++---------- doc/pub/LogReg/html/LogReg.html | 134 ++---------- doc/pub/LogReg/ipynb/LogReg.ipynb | 151 +++---------- doc/pub/LogReg/ipynb/ipynb-LogReg-src.tar.gz | Bin 191 -> 191 bytes doc/pub/LogReg/pdf/LogReg-minted.pdf | Bin 224657 -> 225716 bytes doc/src/LogisticRegression/LogReg.do.txt | 127 ++--------- 27 files changed, 243 insertions(+), 828 deletions(-) diff --git a/doc/pub/LogReg/html/._LogReg-bs000.html b/doc/pub/LogReg/html/._LogReg-bs000.html index 91034fec6..dfe90dfca 100644 --- a/doc/pub/LogReg/html/._LogReg-bs000.html +++ b/doc/pub/LogReg/html/._LogReg-bs000.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • @@ -152,7 +152,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Oct 9, 2019

    +

    Oct 17, 2019


    diff --git a/doc/pub/LogReg/html/._LogReg-bs001.html b/doc/pub/LogReg/html/._LogReg-bs001.html index 675312c5f..824c211c1 100644 --- a/doc/pub/LogReg/html/._LogReg-bs001.html +++ b/doc/pub/LogReg/html/._LogReg-bs001.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({

  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs002.html b/doc/pub/LogReg/html/._LogReg-bs002.html index e6c577b33..7097fb415 100644 --- a/doc/pub/LogReg/html/._LogReg-bs002.html +++ b/doc/pub/LogReg/html/._LogReg-bs002.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs003.html b/doc/pub/LogReg/html/._LogReg-bs003.html index c5d9ed119..8db00fc7b 100644 --- a/doc/pub/LogReg/html/._LogReg-bs003.html +++ b/doc/pub/LogReg/html/._LogReg-bs003.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs004.html b/doc/pub/LogReg/html/._LogReg-bs004.html index 47474cd4d..6a4911b55 100644 --- a/doc/pub/LogReg/html/._LogReg-bs004.html +++ b/doc/pub/LogReg/html/._LogReg-bs004.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs005.html b/doc/pub/LogReg/html/._LogReg-bs005.html index c948727b5..75aac686f 100644 --- a/doc/pub/LogReg/html/._LogReg-bs005.html +++ b/doc/pub/LogReg/html/._LogReg-bs005.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs006.html b/doc/pub/LogReg/html/._LogReg-bs006.html index c96a03492..b8d4885f8 100644 --- a/doc/pub/LogReg/html/._LogReg-bs006.html +++ b/doc/pub/LogReg/html/._LogReg-bs006.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs007.html b/doc/pub/LogReg/html/._LogReg-bs007.html index 4dddc8c43..874b05796 100644 --- a/doc/pub/LogReg/html/._LogReg-bs007.html +++ b/doc/pub/LogReg/html/._LogReg-bs007.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs008.html b/doc/pub/LogReg/html/._LogReg-bs008.html index ee946a1da..b53404eef 100644 --- a/doc/pub/LogReg/html/._LogReg-bs008.html +++ b/doc/pub/LogReg/html/._LogReg-bs008.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs009.html b/doc/pub/LogReg/html/._LogReg-bs009.html index 17e64a71b..777f5fb7c 100644 --- a/doc/pub/LogReg/html/._LogReg-bs009.html +++ b/doc/pub/LogReg/html/._LogReg-bs009.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs010.html b/doc/pub/LogReg/html/._LogReg-bs010.html index 283f54f0a..42c8317e8 100644 --- a/doc/pub/LogReg/html/._LogReg-bs010.html +++ b/doc/pub/LogReg/html/._LogReg-bs010.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs011.html b/doc/pub/LogReg/html/._LogReg-bs011.html index 445ffe058..5af7c5622 100644 --- a/doc/pub/LogReg/html/._LogReg-bs011.html +++ b/doc/pub/LogReg/html/._LogReg-bs011.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs012.html b/doc/pub/LogReg/html/._LogReg-bs012.html index 79af9605f..4b36c3885 100644 --- a/doc/pub/LogReg/html/._LogReg-bs012.html +++ b/doc/pub/LogReg/html/._LogReg-bs012.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs013.html b/doc/pub/LogReg/html/._LogReg-bs013.html index 2db41aa30..91c96e674 100644 --- a/doc/pub/LogReg/html/._LogReg-bs013.html +++ b/doc/pub/LogReg/html/._LogReg-bs013.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs014.html b/doc/pub/LogReg/html/._LogReg-bs014.html index 4c86df9a3..638f66ebb 100644 --- a/doc/pub/LogReg/html/._LogReg-bs014.html +++ b/doc/pub/LogReg/html/._LogReg-bs014.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs015.html b/doc/pub/LogReg/html/._LogReg-bs015.html index f5022e2bb..dab9597c5 100644 --- a/doc/pub/LogReg/html/._LogReg-bs015.html +++ b/doc/pub/LogReg/html/._LogReg-bs015.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs016.html b/doc/pub/LogReg/html/._LogReg-bs016.html index bd33a73b8..25597063d 100644 --- a/doc/pub/LogReg/html/._LogReg-bs016.html +++ b/doc/pub/LogReg/html/._LogReg-bs016.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • diff --git a/doc/pub/LogReg/html/._LogReg-bs017.html b/doc/pub/LogReg/html/._LogReg-bs017.html index c65a62404..c2cd3861a 100644 --- a/doc/pub/LogReg/html/._LogReg-bs017.html +++ b/doc/pub/LogReg/html/._LogReg-bs017.html @@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -117,7 +117,7 @@ MathJax.Hub.Config({
  • More classes
  • A simple classification problem
  • The Credit Card example
    • -
  • Analysis of the credi card example
    • +
  • How to read the Credit Card data
    • @@ -135,210 +135,18 @@ MathJax.Hub.Config({

    The Credit Card example

    Here we use the the credit card data. -The data are from an extensive database from Taiwan and include more than ten predictors. More text and clean up of code will be added. +The data are from an extensive database from Taiwan and include more than ten predictors. +

    +For categorical data -Scikit-Learn- provides a so-called one-hot encoder. +This is called one-hot +encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold). +Scikit-Learn provides a OneHotEncoder encoder to convert integer categorical values into one-hot

    -

    import pandas as pd
-import os
-import numpy as np
-
-
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import OneHotEncoder
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler, OneHotEncoder
-from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score
-
-# Trying to set the seed
-np.random.seed(0)
-import random
-random.seed(0)
-
-# Reading file into data frame
-cwd = os.getcwd()
-filename = cwd + '/default of credit card clients.xls'
-nanDict = {}
-df = pd.read_excel(filename, header=1, skiprows=0, index_col=0, na_values=nanDict)
-
-df.rename(index=str, columns={"default payment next month": "defaultPaymentNextMonth"}, inplace=True)
-
-# Features and targets 
-X = df.loc[:, df.columns != 'defaultPaymentNextMonth'].values
-y = df.loc[:, df.columns == 'defaultPaymentNextMonth'].values
-
-# Categorical variables to one-hot's
-onehotencoder = OneHotEncoder(categories="auto")
-
-X = ColumnTransformer(
-    [("", onehotencoder, [3]),],
-    remainder="passthrough"
-).fit_transform(X)
-
-y.shape
-
-# Train-test split
-trainingShare = 0.5 
-seed  = 1
-XTrain, XTest, yTrain, yTest=train_test_split(X, y, train_size=trainingShare, \
-                                              test_size = 1-trainingShare,
-                                             random_state=seed)
-
-# Input Scaling
-sc = StandardScaler()
-XTrain = sc.fit_transform(XTrain)
-XTest = sc.transform(XTest)
-
-# One-hot's of the target vector
-Y_train_onehot, Y_test_onehot = onehotencoder.fit_transform(yTrain), onehotencoder.fit_transform(yTest)
-
-# Remove instances with zeros only for past bill statements or paid amounts
-'''
-df = df.drop(df[(df.BILL_AMT1 == 0) &
-                (df.BILL_AMT2 == 0) &
-                (df.BILL_AMT3 == 0) &
-                (df.BILL_AMT4 == 0) &
-                (df.BILL_AMT5 == 0) &
-                (df.BILL_AMT6 == 0) &
-                (df.PAY_AMT1 == 0) &
-                (df.PAY_AMT2 == 0) &
-                (df.PAY_AMT3 == 0) &
-                (df.PAY_AMT4 == 0) &
-                (df.PAY_AMT5 == 0) &
-                (df.PAY_AMT6 == 0)].index)
-'''
-df = df.drop(df[(df.BILL_AMT1 == 0) &
-                (df.BILL_AMT2 == 0) &
-                (df.BILL_AMT3 == 0) &
-                (df.BILL_AMT4 == 0) &
-                (df.BILL_AMT5 == 0) &
-                (df.BILL_AMT6 == 0)].index)
-
-df = df.drop(df[(df.PAY_AMT1 == 0) &
-                (df.PAY_AMT2 == 0) &
-                (df.PAY_AMT3 == 0) &
-                (df.PAY_AMT4 == 0) &
-                (df.PAY_AMT5 == 0) &
-                (df.PAY_AMT6 == 0)].index)
-
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import GridSearchCV
-
-lambdas=np.logspace(-5,7,13)
-parameters = [{'C': 1./lambdas, "solver":["lbfgs"]}]#*len(parameters)}]
-scoring = ['accuracy', 'roc_auc']
-logReg = LogisticRegression()
-gridSearch = GridSearchCV(logReg, parameters, cv=5, scoring=scoring, refit='roc_auc') 
-
    -

    - - -

    # "refit" gives the metric used deciding best model. 
-# See more http://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html
-gridSearch.fit(XTrain, yTrain.ravel())
-
-def gridSearchSummary(method, scoring):
-    """Prints best parameters from Grid search
-    and AUC with standard deviation for all 
-    parameter combos """
-    
-    method = eval(method)
-    if scoring == 'accuracy':
-        mean = 'mean_test_score'
-        sd = 'std_test_score'
-    elif scoring == 'auc':
-        mean = 'mean_test_roc_auc'
-        sd = 'std_test_roc_auc'
-    print("Best: %f using %s" % (method.best_score_, method.best_params_))
-    means = method.cv_results_[mean]
-    stds = method.cv_results_[sd]
-    params = method.cv_results_['params']
-    for mean, stdev, param in zip(means, stds, params):
-        print("%f (%f) with: %r" % (mean, stdev, param))
-
-def createConfusionMatrix(method, printOut=True):
-    """
-    Computes and prints confusion matrices, accuracy scores,
-    and AUC for test and training sets 
-    """
-    confusionArray = np.zeros(6, dtype=object)
-    method = eval(method)
-    
-    # Train
-    yPredTrain = method.predict(XTrain)
-    yPredTrain = (yPredTrain > 0.5)
-    cm = confusion_matrix(
-        yTrain, yPredTrain) 
-    cm = np.around(cm/cm.sum(axis=1)[:,None], 2)
-    confusionArray[0] = cm
-    
-    accScore = accuracy_score(yTrain, yPredTrain)
-    confusionArray[1] = accScore
-    
-    AUC = roc_auc_score(yTrain, yPredTrain)
-    confusionArray[2] = AUC
-    
-    if printOut:
-        print('\n###################  Training  ###############')
-        print('\nTraining Confusion matrix: \n', cm)
-        print('\nTraining Accuracy score: \n', accScore)
-        print('\nTrain AUC: \n', AUC)
-    
-    # Test
-    yPred = method.predict(XTest)
-    yPred = (yPred > 0.5)
-    cm = confusion_matrix(
-        yTest, yPred) 
-    cm = np.around(cm/cm.sum(axis=1)[:,None], 2)
-    confusionArray[3] = cm
-    
-    accScore = accuracy_score(yTest, yPred)
-    confusionArray[4] = accScore
-    
-    AUC = roc_auc_score(yTest, yPred)
-    confusionArray[5] = AUC
-    
-    if printOut:
-        print('\n###################  Testing  ###############')
-        print('\nTest Confusion matrix: \n', cm)
-        print('\nTest Accuracy score: \n', accScore)
-        print('\nTestAUC: \n', AUC)    
-    
-    return confusionArray
-
-
-import matplotlib.pyplot as plt
-import seaborn
-import scikitplot as skplt
-
-seaborn.set(style="white", context="notebook", font_scale=1.5, 
-            rc={"axes.grid": True, "legend.frameon": False,
-"lines.markeredgewidth": 1.4, "lines.markersize": 10})
-seaborn.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 4.5})
-
-yPred = gridSearch.predict_proba(XTest) 
-print(yTest.ravel().shape, yPred.shape)
-
-#skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred_onehot)
-skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred)
-
-defaults = sum(yTest == 1)
-total = len(yTest)
-defaultRate = defaults/total
-def bestCurve(defaults, total, defaultRate):
-    x = np.linspace(0, 1, total)
-    
-    y1 = np.linspace(0, 1, defaults)
-    y2 = np.ones(total-defaults)
-    y3 = np.concatenate([y1,y2])
-    return x, y3
-
-x, best = bestCurve(defaults=defaults, total=total, defaultRate=defaultRate)    
-plt.plot(x, best)    
-
-
-plt.show()
+
    from sklearn.preprocessing import OneHotEncoder
+encoder = OneHotEncoder()
 
    
 
    
 
    
diff --git a/doc/pub/LogReg/html/._LogReg-bs018.html b/doc/pub/LogReg/html/._LogReg-bs018.html
index 035c9ef3a..5cc27a194 100644
--- a/doc/pub/LogReg/html/._LogReg-bs018.html
+++ b/doc/pub/LogReg/html/._LogReg-bs018.html
@@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source
               ('More classes', 2, None, '___sec14'),
               ('A simple classification problem', 2, None, '___sec15'),
               ('The Credit Card example', 2, None, '___sec16'),
-              ('Analysis of the credi card example', 2, None, '___sec17')]}
+              ('How to read the Credit Card data', 2, None, '___sec17')]}
 end of tocinfo -->
 
 
@@ -117,7 +117,7 @@ MathJax.Hub.Config({
       
  • More classes
    • 
       
  • A simple classification problem
    • 
       
  • The Credit Card example
    • 
-      
  • Analysis of the credi card example
    • 
+      
  • How to read the Credit Card data
    • 
 
         
       
@@ -133,7 +133,103 @@ MathJax.Hub.Config({
 
 
 
-
    Analysis of the credi card example 
    
+
    How to read the Credit Card data 
    
+
+
    
+
+
+
    import pandas as pd
+import os
+import numpy as np
+
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score
+
+# Trying to set the seed
+np.random.seed(0)
+import random
+random.seed(0)
+
+# Reading file into data frame
+cwd = os.getcwd()
+filename = cwd + '/default of credit card clients.xls'
+nanDict = {}
+df = pd.read_excel(filename, header=1, skiprows=0, index_col=0, na_values=nanDict)
+
+df.rename(index=str, columns={"default payment next month": "defaultPaymentNextMonth"}, inplace=True)
+
+# Features and targets 
+X = df.loc[:, df.columns != 'defaultPaymentNextMonth'].values
+y = df.loc[:, df.columns == 'defaultPaymentNextMonth'].values
+
+# Categorical variables to one-hot's
+onehotencoder = OneHotEncoder(categories="auto")
+
+X = ColumnTransformer(
+    [("", onehotencoder, [3]),],
+    remainder="passthrough"
+).fit_transform(X)
+
+y.shape
+
+# Train-test split
+trainingShare = 0.5 
+seed  = 1
+XTrain, XTest, yTrain, yTest=train_test_split(X, y, train_size=trainingShare, \
+                                              test_size = 1-trainingShare,
+                                             random_state=seed)
+
+# Input Scaling
+sc = StandardScaler()
+XTrain = sc.fit_transform(XTrain)
+XTest = sc.transform(XTest)
+
+# One-hot's of the target vector
+Y_train_onehot, Y_test_onehot = onehotencoder.fit_transform(yTrain), onehotencoder.fit_transform(yTest)
+
+# Remove instances with zeros only for past bill statements or paid amounts
+'''
+df = df.drop(df[(df.BILL_AMT1 == 0) &
+                (df.BILL_AMT2 == 0) &
+                (df.BILL_AMT3 == 0) &
+                (df.BILL_AMT4 == 0) &
+                (df.BILL_AMT5 == 0) &
+                (df.BILL_AMT6 == 0) &
+                (df.PAY_AMT1 == 0) &
+                (df.PAY_AMT2 == 0) &
+                (df.PAY_AMT3 == 0) &
+                (df.PAY_AMT4 == 0) &
+                (df.PAY_AMT5 == 0) &
+                (df.PAY_AMT6 == 0)].index)
+'''
+df = df.drop(df[(df.BILL_AMT1 == 0) &
+                (df.BILL_AMT2 == 0) &
+                (df.BILL_AMT3 == 0) &
+                (df.BILL_AMT4 == 0) &
+                (df.BILL_AMT5 == 0) &
+                (df.BILL_AMT6 == 0)].index)
+
+df = df.drop(df[(df.PAY_AMT1 == 0) &
+                (df.PAY_AMT2 == 0) &
+                (df.PAY_AMT3 == 0) &
+                (df.PAY_AMT4 == 0) &
+                (df.PAY_AMT5 == 0) &
+                (df.PAY_AMT6 == 0)].index)
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV
+
+lambdas=np.logspace(-5,7,13)
+parameters = [{'C': 1./lambdas, "solver":["lbfgs"]}]#*len(parameters)}]
+scoring = ['accuracy', 'roc_auc']
+logReg = LogisticRegression()
+gridSearch = GridSearchCV(logReg, parameters, cv=5, scoring=scoring, refit='roc_auc') 
+
    
+
    
 
 
    
 
diff --git a/doc/pub/LogReg/html/LogReg-bs.html b/doc/pub/LogReg/html/LogReg-bs.html
index 91034fec6..dfe90dfca 100644
--- a/doc/pub/LogReg/html/LogReg-bs.html
+++ b/doc/pub/LogReg/html/LogReg-bs.html
@@ -62,7 +62,7 @@ Automatically generated HTML file from DocOnce source
               ('More classes', 2, None, '___sec14'),
               ('A simple classification problem', 2, None, '___sec15'),
               ('The Credit Card example', 2, None, '___sec16'),
-              ('Analysis of the credi card example', 2, None, '___sec17')]}
+              ('How to read the Credit Card data', 2, None, '___sec17')]}
 end of tocinfo -->
 
 
@@ -117,7 +117,7 @@ MathJax.Hub.Config({
       
  • More classes
    • 
       
  • A simple classification problem
    • 
       
  • The Credit Card example
    • 
-      
  • Analysis of the credi card example
    • 
+      
  • How to read the Credit Card data
    • 
 
         
       
@@ -152,7 +152,7 @@ MathJax.Hub.Config({
 
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
    
 
    
 
    
-
    Oct 9, 2019
     
+
    Oct 17, 2019
     
 
    
 
    
 
    
diff --git a/doc/pub/LogReg/html/LogReg-reveal.html b/doc/pub/LogReg/html/LogReg-reveal.html
index 6c7b64008..101656602 100644
--- a/doc/pub/LogReg/html/LogReg-reveal.html
+++ b/doc/pub/LogReg/html/LogReg-reveal.html
@@ -148,7 +148,7 @@ MathJax.Hub.Config({
 
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
    
 
    
 
     
    
-
    Oct 9, 2019
     
+
    Oct 17, 2019
     
 
    
 
    
 
@@ -668,7 +668,24 @@ methods.
 
    
 
    The Credit Card example 
    
 Here we use the the credit card data. 
-The data are from an extensive database from Taiwan and include more than ten predictors.   More text and clean up of code will be added.
+The data are from an extensive database from Taiwan and include more than ten predictors.
+
+
    
+For categorical data -Scikit-Learn- provides a so-called one-hot encoder.
+This is called one-hot
+encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold).
+Scikit-Learn provides a OneHotEncoder encoder to convert integer categorical values into one-hot
+
    
+
+
+
    from sklearn.preprocessing import OneHotEncoder
+encoder = OneHotEncoder()
+
    
+
    
+
+
+
    
+
    How to read the Credit Card data 
    
 
 
    
 
@@ -764,120 +781,6 @@ scoring = ['accuracy', 5, scoring=scoring, refit='roc_auc')
    -

    - - -

    # "refit" gives the metric used deciding best model. 
-# See more http://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html
-gridSearch.fit(XTrain, yTrain.ravel())
-
-def gridSearchSummary(method, scoring):
-    """Prints best parameters from Grid search
-    and AUC with standard deviation for all 
-    parameter combos """
-    
-    method = eval(method)
-    if scoring == 'accuracy':
-        mean = 'mean_test_score'
-        sd = 'std_test_score'
-    elif scoring == 'auc':
-        mean = 'mean_test_roc_auc'
-        sd = 'std_test_roc_auc'
-    print("Best: %f using %s" % (method.best_score_, method.best_params_))
-    means = method.cv_results_[mean]
-    stds = method.cv_results_[sd]
-    params = method.cv_results_['params']
-    for mean, stdev, param in zip(means, stds, params):
-        print("%f (%f) with: %r" % (mean, stdev, param))
-
-def createConfusionMatrix(method, printOut=True):
-    """
-    Computes and prints confusion matrices, accuracy scores,
-    and AUC for test and training sets 
-    """
-    confusionArray = np.zeros(6, dtype=object)
-    method = eval(method)
-    
-    # Train
-    yPredTrain = method.predict(XTrain)
-    yPredTrain = (yPredTrain > 0.5)
-    cm = confusion_matrix(
-        yTrain, yPredTrain) 
-    cm = np.around(cm/cm.sum(axis=1)[:,None], 2)
-    confusionArray[0] = cm
-    
-    accScore = accuracy_score(yTrain, yPredTrain)
-    confusionArray[1] = accScore
-    
-    AUC = roc_auc_score(yTrain, yPredTrain)
-    confusionArray[2] = AUC
-    
-    if printOut:
-        print('\n###################  Training  ###############')
-        print('\nTraining Confusion matrix: \n', cm)
-        print('\nTraining Accuracy score: \n', accScore)
-        print('\nTrain AUC: \n', AUC)
-    
-    # Test
-    yPred = method.predict(XTest)
-    yPred = (yPred > 0.5)
-    cm = confusion_matrix(
-        yTest, yPred) 
-    cm = np.around(cm/cm.sum(axis=1)[:,None], 2)
-    confusionArray[3] = cm
-    
-    accScore = accuracy_score(yTest, yPred)
-    confusionArray[4] = accScore
-    
-    AUC = roc_auc_score(yTest, yPred)
-    confusionArray[5] = AUC
-    
-    if printOut:
-        print('\n###################  Testing  ###############')
-        print('\nTest Confusion matrix: \n', cm)
-        print('\nTest Accuracy score: \n', accScore)
-        print('\nTestAUC: \n', AUC)    
-    
-    return confusionArray
-
-
-import matplotlib.pyplot as plt
-import seaborn
-import scikitplot as skplt
-
-seaborn.set(style="white", context="notebook", font_scale=1.5, 
-            rc={"axes.grid": True, "legend.frameon": False,
-"lines.markeredgewidth": 1.4, "lines.markersize": 10})
-seaborn.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 4.5})
-
-yPred = gridSearch.predict_proba(XTest) 
-print(yTest.ravel().shape, yPred.shape)
-
-#skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred_onehot)
-skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred)
-
-defaults = sum(yTest == 1)
-total = len(yTest)
-defaultRate = defaults/total
-def bestCurve(defaults, total, defaultRate):
-    x = np.linspace(0, 1, total)
-    
-    y1 = np.linspace(0, 1, defaults)
-    y2 = np.ones(total-defaults)
-    y3 = np.concatenate([y1,y2])
-    return x, y3
-
-x, best = bestCurve(defaults=defaults, total=total, defaultRate=defaultRate)    
-plt.plot(x, best)    
-
-
-plt.show()
-
    - - - -
    -

    Analysis of the credi card example

    diff --git a/doc/pub/LogReg/html/LogReg-solarized.html b/doc/pub/LogReg/html/LogReg-solarized.html index ba2b80950..3b26ca41c 100644 --- a/doc/pub/LogReg/html/LogReg-solarized.html +++ b/doc/pub/LogReg/html/LogReg-solarized.html @@ -56,7 +56,7 @@ div { text-align: justify; text-justify: inter-word; } ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -98,7 +98,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Oct 9, 2019

    +

    Oct 17, 2019


    @@ -570,7 +570,23 @@ methods.

    The Credit Card example

    Here we use the the credit card data. -The data are from an extensive database from Taiwan and include more than ten predictors. More text and clean up of code will be added. +The data are from an extensive database from Taiwan and include more than ten predictors. + +

    +For categorical data -Scikit-Learn- provides a so-called one-hot encoder. +This is called one-hot +encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold). +Scikit-Learn provides a OneHotEncoder encoder to convert integer categorical values into one-hot +

    + + +

    from sklearn.preprocessing import OneHotEncoder
+encoder = OneHotEncoder()
+
    +

    +









    + +

    How to read the Credit Card data

    @@ -668,118 +684,6 @@ gridSearch = GridSearchCV(logReg, parameters, cv=5<

    - -

    # "refit" gives the metric used deciding best model. 
-# See more http://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html
-gridSearch.fit(XTrain, yTrain.ravel())
-
-def gridSearchSummary(method, scoring):
-    """Prints best parameters from Grid search
-    and AUC with standard deviation for all 
-    parameter combos """
-    
-    method = eval(method)
-    if scoring == 'accuracy':
-        mean = 'mean_test_score'
-        sd = 'std_test_score'
-    elif scoring == 'auc':
-        mean = 'mean_test_roc_auc'
-        sd = 'std_test_roc_auc'
-    print("Best: %f using %s" % (method.best_score_, method.best_params_))
-    means = method.cv_results_[mean]
-    stds = method.cv_results_[sd]
-    params = method.cv_results_['params']
-    for mean, stdev, param in zip(means, stds, params):
-        print("%f (%f) with: %r" % (mean, stdev, param))
-
-def createConfusionMatrix(method, printOut=True):
-    """
-    Computes and prints confusion matrices, accuracy scores,
-    and AUC for test and training sets 
-    """
-    confusionArray = np.zeros(6, dtype=object)
-    method = eval(method)
-    
-    # Train
-    yPredTrain = method.predict(XTrain)
-    yPredTrain = (yPredTrain > 0.5)
-    cm = confusion_matrix(
-        yTrain, yPredTrain) 
-    cm = np.around(cm/cm.sum(axis=1)[:,None], 2)
-    confusionArray[0] = cm
-    
-    accScore = accuracy_score(yTrain, yPredTrain)
-    confusionArray[1] = accScore
-    
-    AUC = roc_auc_score(yTrain, yPredTrain)
-    confusionArray[2] = AUC
-    
-    if printOut:
-        print('\n###################  Training  ###############')
-        print('\nTraining Confusion matrix: \n', cm)
-        print('\nTraining Accuracy score: \n', accScore)
-        print('\nTrain AUC: \n', AUC)
-    
-    # Test
-    yPred = method.predict(XTest)
-    yPred = (yPred > 0.5)
-    cm = confusion_matrix(
-        yTest, yPred) 
-    cm = np.around(cm/cm.sum(axis=1)[:,None], 2)
-    confusionArray[3] = cm
-    
-    accScore = accuracy_score(yTest, yPred)
-    confusionArray[4] = accScore
-    
-    AUC = roc_auc_score(yTest, yPred)
-    confusionArray[5] = AUC
-    
-    if printOut:
-        print('\n###################  Testing  ###############')
-        print('\nTest Confusion matrix: \n', cm)
-        print('\nTest Accuracy score: \n', accScore)
-        print('\nTestAUC: \n', AUC)    
-    
-    return confusionArray
-
-
-import matplotlib.pyplot as plt
-import seaborn
-import scikitplot as skplt
-
-seaborn.set(style="white", context="notebook", font_scale=1.5, 
-            rc={"axes.grid": True, "legend.frameon": False,
-"lines.markeredgewidth": 1.4, "lines.markersize": 10})
-seaborn.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 4.5})
-
-yPred = gridSearch.predict_proba(XTest) 
-print(yTest.ravel().shape, yPred.shape)
-
-#skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred_onehot)
-skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred)
-
-defaults = sum(yTest == 1)
-total = len(yTest)
-defaultRate = defaults/total
-def bestCurve(defaults, total, defaultRate):
-    x = np.linspace(0, 1, total)
-    
-    y1 = np.linspace(0, 1, defaults)
-    y2 = np.ones(total-defaults)
-    y3 = np.concatenate([y1,y2])
-    return x, y3
-
-x, best = bestCurve(defaults=defaults, total=total, defaultRate=defaultRate)    
-plt.plot(x, best)    
-
-
-plt.show()
-
    -

    -









    - -

    Analysis of the credi card example

    - diff --git a/doc/pub/LogReg/html/LogReg.html b/doc/pub/LogReg/html/LogReg.html index dd6403806..22e15425c 100644 --- a/doc/pub/LogReg/html/LogReg.html +++ b/doc/pub/LogReg/html/LogReg.html @@ -61,7 +61,7 @@ div { text-align: justify; text-justify: inter-word; } ('More classes', 2, None, '___sec14'), ('A simple classification problem', 2, None, '___sec15'), ('The Credit Card example', 2, None, '___sec16'), - ('Analysis of the credi card example', 2, None, '___sec17')]} + ('How to read the Credit Card data', 2, None, '___sec17')]} end of tocinfo --> @@ -103,7 +103,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Oct 9, 2019

    +

    Oct 17, 2019


    @@ -575,7 +575,23 @@ methods.

    The Credit Card example

    Here we use the the credit card data. -The data are from an extensive database from Taiwan and include more than ten predictors. More text and clean up of code will be added. +The data are from an extensive database from Taiwan and include more than ten predictors. + +

    +For categorical data -Scikit-Learn- provides a so-called one-hot encoder. +This is called one-hot +encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold). +Scikit-Learn provides a OneHotEncoder encoder to convert integer categorical values into one-hot +

    + + +

    from sklearn.preprocessing import OneHotEncoder
+encoder = OneHotEncoder()
+
    +

    +









    + +

    How to read the Credit Card data

    @@ -673,118 +689,6 @@ gridSearch = GridSearchCV(logReg, parameters

    - -

    # "refit" gives the metric used deciding best model. 
-# See more http://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html
-gridSearch.fit(XTrain, yTrain.ravel())
-
-def gridSearchSummary(method, scoring):
-    """Prints best parameters from Grid search
-    and AUC with standard deviation for all 
-    parameter combos """
-    
-    method = eval(method)
-    if scoring == 'accuracy':
-        mean = 'mean_test_score'
-        sd = 'std_test_score'
-    elif scoring == 'auc':
-        mean = 'mean_test_roc_auc'
-        sd = 'std_test_roc_auc'
-    print("Best: %f using %s" % (method.best_score_, method.best_params_))
-    means = method.cv_results_[mean]
-    stds = method.cv_results_[sd]
-    params = method.cv_results_['params']
-    for mean, stdev, param in zip(means, stds, params):
-        print("%f (%f) with: %r" % (mean, stdev, param))
-
-def createConfusionMatrix(method, printOut=True):
-    """
-    Computes and prints confusion matrices, accuracy scores,
-    and AUC for test and training sets 
-    """
-    confusionArray = np.zeros(6, dtype=object)
-    method = eval(method)
-    
-    # Train
-    yPredTrain = method.predict(XTrain)
-    yPredTrain = (yPredTrain > 0.5)
-    cm = confusion_matrix(
-        yTrain, yPredTrain) 
-    cm = np.around(cm/cm.sum(axis=1)[:,None], 2)
-    confusionArray[0] = cm
-    
-    accScore = accuracy_score(yTrain, yPredTrain)
-    confusionArray[1] = accScore
-    
-    AUC = roc_auc_score(yTrain, yPredTrain)
-    confusionArray[2] = AUC
-    
-    if printOut:
-        print('\n###################  Training  ###############')
-        print('\nTraining Confusion matrix: \n', cm)
-        print('\nTraining Accuracy score: \n', accScore)
-        print('\nTrain AUC: \n', AUC)
-    
-    # Test
-    yPred = method.predict(XTest)
-    yPred = (yPred > 0.5)
-    cm = confusion_matrix(
-        yTest, yPred) 
-    cm = np.around(cm/cm.sum(axis=1)[:,None], 2)
-    confusionArray[3] = cm
-    
-    accScore = accuracy_score(yTest, yPred)
-    confusionArray[4] = accScore
-    
-    AUC = roc_auc_score(yTest, yPred)
-    confusionArray[5] = AUC
-    
-    if printOut:
-        print('\n###################  Testing  ###############')
-        print('\nTest Confusion matrix: \n', cm)
-        print('\nTest Accuracy score: \n', accScore)
-        print('\nTestAUC: \n', AUC)    
-    
-    return confusionArray
-
-
-import matplotlib.pyplot as plt
-import seaborn
-import scikitplot as skplt
-
-seaborn.set(style="white", context="notebook", font_scale=1.5, 
-            rc={"axes.grid": True, "legend.frameon": False,
-"lines.markeredgewidth": 1.4, "lines.markersize": 10})
-seaborn.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 4.5})
-
-yPred = gridSearch.predict_proba(XTest) 
-print(yTest.ravel().shape, yPred.shape)
-
-#skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred_onehot)
-skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred)
-
-defaults = sum(yTest == 1)
-total = len(yTest)
-defaultRate = defaults/total
-def bestCurve(defaults, total, defaultRate):
-    x = np.linspace(0, 1, total)
-    
-    y1 = np.linspace(0, 1, defaults)
-    y2 = np.ones(total-defaults)
-    y3 = np.concatenate([y1,y2])
-    return x, y3
-
-x, best = bestCurve(defaults=defaults, total=total, defaultRate=defaultRate)    
-plt.plot(x, best)    
-
-
-plt.show()
-
    -

    -









    - -

    Analysis of the credi card example

    - diff --git a/doc/pub/LogReg/ipynb/LogReg.ipynb b/doc/pub/LogReg/ipynb/LogReg.ipynb index cb31cc32e..2566f60e9 100644 --- a/doc/pub/LogReg/ipynb/LogReg.ipynb +++ b/doc/pub/LogReg/ipynb/LogReg.ipynb @@ -10,7 +10,7 @@ " \n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Oct 9, 2019**\n", + "Date: **Oct 17, 2019**\n", "\n", "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", @@ -669,7 +669,12 @@ "source": [ "## The Credit Card example\n", "Here we use the the [credit card data](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients). \n", - "The data are from an extensive database from Taiwan and include more than ten predictors. More text and clean up of code will be added." + "The data are from an extensive database from Taiwan and include more than ten predictors.\n", + "\n", + "For categorical data -Scikit-Learn- provides a so-called **one-hot encoder**.\n", + "This is called one-hot\n", + "encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold).\n", + "**Scikit-Learn** provides a OneHotEncoder encoder to convert integer categorical values into one-hot" ] }, { @@ -679,6 +684,25 @@ "collapsed": false }, "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "encoder = OneHotEncoder()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to read the Credit Card data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "import pandas as pd\n", "import os\n", @@ -771,129 +795,6 @@ "logReg = LogisticRegression()\n", "gridSearch = GridSearchCV(logReg, parameters, cv=5, scoring=scoring, refit='roc_auc')" ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "# \"refit\" gives the metric used deciding best model. \n", - "# See more http://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html\n", - "gridSearch.fit(XTrain, yTrain.ravel())\n", - "\n", - "def gridSearchSummary(method, scoring):\n", - " \"\"\"Prints best parameters from Grid search\n", - " and AUC with standard deviation for all \n", - " parameter combos \"\"\"\n", - " \n", - " method = eval(method)\n", - " if scoring == 'accuracy':\n", - " mean = 'mean_test_score'\n", - " sd = 'std_test_score'\n", - " elif scoring == 'auc':\n", - " mean = 'mean_test_roc_auc'\n", - " sd = 'std_test_roc_auc'\n", - " print(\"Best: %f using %s\" % (method.best_score_, method.best_params_))\n", - " means = method.cv_results_[mean]\n", - " stds = method.cv_results_[sd]\n", - " params = method.cv_results_['params']\n", - " for mean, stdev, param in zip(means, stds, params):\n", - " print(\"%f (%f) with: %r\" % (mean, stdev, param))\n", - "\n", - "def createConfusionMatrix(method, printOut=True):\n", - " \"\"\"\n", - " Computes and prints confusion matrices, accuracy scores,\n", - " and AUC for test and training sets \n", - " \"\"\"\n", - " confusionArray = np.zeros(6, dtype=object)\n", - " method = eval(method)\n", - " \n", - " # Train\n", - " yPredTrain = method.predict(XTrain)\n", - " yPredTrain = (yPredTrain > 0.5)\n", - " cm = confusion_matrix(\n", - " yTrain, yPredTrain) \n", - " cm = np.around(cm/cm.sum(axis=1)[:,None], 2)\n", - " confusionArray[0] = cm\n", - " \n", - " accScore = accuracy_score(yTrain, yPredTrain)\n", - " confusionArray[1] = accScore\n", - " \n", - " AUC = roc_auc_score(yTrain, yPredTrain)\n", - " confusionArray[2] = AUC\n", - " \n", - " if printOut:\n", - " print('\\n################### Training ###############')\n", - " print('\\nTraining Confusion matrix: \\n', cm)\n", - " print('\\nTraining Accuracy score: \\n', accScore)\n", - " print('\\nTrain AUC: \\n', AUC)\n", - " \n", - " # Test\n", - " yPred = method.predict(XTest)\n", - " yPred = (yPred > 0.5)\n", - " cm = confusion_matrix(\n", - " yTest, yPred) \n", - " cm = np.around(cm/cm.sum(axis=1)[:,None], 2)\n", - " confusionArray[3] = cm\n", - " \n", - " accScore = accuracy_score(yTest, yPred)\n", - " confusionArray[4] = accScore\n", - " \n", - " AUC = roc_auc_score(yTest, yPred)\n", - " confusionArray[5] = AUC\n", - " \n", - " if printOut:\n", - " print('\\n################### Testing ###############')\n", - " print('\\nTest Confusion matrix: \\n', cm)\n", - " print('\\nTest Accuracy score: \\n', accScore)\n", - " print('\\nTestAUC: \\n', AUC) \n", - " \n", - " return confusionArray\n", - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn\n", - "import scikitplot as skplt\n", - "\n", - "seaborn.set(style=\"white\", context=\"notebook\", font_scale=1.5, \n", - " rc={\"axes.grid\": True, \"legend.frameon\": False,\n", - "\"lines.markeredgewidth\": 1.4, \"lines.markersize\": 10})\n", - "seaborn.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 4.5})\n", - "\n", - "yPred = gridSearch.predict_proba(XTest) \n", - "print(yTest.ravel().shape, yPred.shape)\n", - "\n", - "#skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred_onehot)\n", - "skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred)\n", - "\n", - "defaults = sum(yTest == 1)\n", - "total = len(yTest)\n", - "defaultRate = defaults/total\n", - "def bestCurve(defaults, total, defaultRate):\n", - " x = np.linspace(0, 1, total)\n", - " \n", - " y1 = np.linspace(0, 1, defaults)\n", - " y2 = np.ones(total-defaults)\n", - " y3 = np.concatenate([y1,y2])\n", - " return x, y3\n", - "\n", - "x, best = bestCurve(defaults=defaults, total=total, defaultRate=defaultRate) \n", - "plt.plot(x, best) \n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analysis of the credi card example" - ] } ], "metadata": {}, diff --git a/doc/pub/LogReg/ipynb/ipynb-LogReg-src.tar.gz b/doc/pub/LogReg/ipynb/ipynb-LogReg-src.tar.gz index 120b7e1c00fb3176bb5d84a59020e59bdb808ab0..3aff17299b77d4109836c62b2ce4a2fce78273e6 100644 GIT binary patch literal 191 zcmV;w06_mAiwFRTTc}+C1MSaC3c@fD2H>uHia9|^+NQ1ryATwlc!89pZG2>El7hXx zeSoeMH${Yeo1bBZVW!B}n|&U+y9*XW2+7hE#$07=N)pfYgi;1HVJs$$Wt0M;Ny2CV zWVw@GI_tO@PH9e3TdCj94P#aHVbA;uJo8T+8)ad8=v{*vG&)%#o?(Y|A?rxBy)HqZ tv||g5ymiV*;JODcgmMZszl5#9+VHE5!e2k*d7kHe?E#7%yqEw8002t-RaO81 literal 191 zcmV;w06_mAiwFQkd!1bX1MSaC3c@fD2H>uHia9|^nxEl7hXx zeSoeMH${Yeo1bBZVJ6?K*85H5?k-piAtX%_7&A-xlq8<(38fq;<4G20oD&X&GM2Ic z$Z{vWbk+$ooYI`6wo<>H8^)^Y!=CvSc;=rtHp;^G(7OgTXmqkbJi`v_T-K3ldtE?@ t(vB@K^42LMLFgVtSt_Sci%ZxVtc|$ZDE##^p67Yq*B<|r>_ZOpzKce%Ac@e2s2sv!3I_oB%W$OLu?00LfyrNEUy6k^GdjD(W7BuE;ERjmmU>af 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b/doc/src/LogisticRegression/LogReg.do.txt @@ -451,7 +451,19 @@ if __name__ == "__main__": !split ===== The Credit Card example ===== Here we use the the "credit card data":"https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients". -The data are from an extensive database from Taiwan and include more than ten predictors. More text and clean up of code will be added. +The data are from an extensive database from Taiwan and include more than ten predictors. + +For categorical data -Scikit-Learn- provides a so-called _one-hot encoder_. +This is called one-hot +encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold). +_Scikit-Learn_ provides a OneHotEncoder encoder to convert integer categorical values into one-hot +!bc pycod +from sklearn.preprocessing import OneHotEncoder +encoder = OneHotEncoder() +!ec + +!split +===== How to read the Credit Card data ===== !bc pycod import pandas as pd @@ -546,116 +558,3 @@ logReg = LogisticRegression() gridSearch = GridSearchCV(logReg, parameters, cv=5, scoring=scoring, refit='roc_auc') !ec -!bc pycod - -# "refit" gives the metric used deciding best model. -# See more http://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html -gridSearch.fit(XTrain, yTrain.ravel()) - -def gridSearchSummary(method, scoring): - """Prints best parameters from Grid search - and AUC with standard deviation for all - parameter combos """ - - method = eval(method) - if scoring == 'accuracy': - mean = 'mean_test_score' - sd = 'std_test_score' - elif scoring == 'auc': - mean = 'mean_test_roc_auc' - sd = 'std_test_roc_auc' - print("Best: %f using %s" % (method.best_score_, method.best_params_)) - means = method.cv_results_[mean] - stds = method.cv_results_[sd] - params = method.cv_results_['params'] - for mean, stdev, param in zip(means, stds, params): - print("%f (%f) with: %r" % (mean, stdev, param)) - -def createConfusionMatrix(method, printOut=True): - """ - Computes and prints confusion matrices, accuracy scores, - and AUC for test and training sets - """ - confusionArray = np.zeros(6, dtype=object) - method = eval(method) - - # Train - yPredTrain = method.predict(XTrain) - yPredTrain = (yPredTrain > 0.5) - cm = confusion_matrix( - yTrain, yPredTrain) - cm = np.around(cm/cm.sum(axis=1)[:,None], 2) - confusionArray[0] = cm - - accScore = accuracy_score(yTrain, yPredTrain) - confusionArray[1] = accScore - - AUC = roc_auc_score(yTrain, yPredTrain) - confusionArray[2] = AUC - - if printOut: - print('\n################### Training ###############') - print('\nTraining Confusion matrix: \n', cm) - print('\nTraining Accuracy score: \n', accScore) - print('\nTrain AUC: \n', AUC) - - # Test - yPred = method.predict(XTest) - yPred = (yPred > 0.5) - cm = confusion_matrix( - yTest, yPred) - cm = np.around(cm/cm.sum(axis=1)[:,None], 2) - confusionArray[3] = cm - - accScore = accuracy_score(yTest, yPred) - confusionArray[4] = accScore - - AUC = roc_auc_score(yTest, yPred) - confusionArray[5] = AUC - - if printOut: - print('\n################### Testing ###############') - print('\nTest Confusion matrix: \n', cm) - print('\nTest Accuracy score: \n', accScore) - print('\nTestAUC: \n', AUC) - - return confusionArray - - -import matplotlib.pyplot as plt -import seaborn -import scikitplot as skplt - -seaborn.set(style="white", context="notebook", font_scale=1.5, - rc={"axes.grid": True, "legend.frameon": False, -"lines.markeredgewidth": 1.4, "lines.markersize": 10}) -seaborn.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 4.5}) - -yPred = gridSearch.predict_proba(XTest) -print(yTest.ravel().shape, yPred.shape) - -#skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred_onehot) -skplt.metrics.plot_cumulative_gain(yTest.ravel(), yPred) - -defaults = sum(yTest == 1) -total = len(yTest) -defaultRate = defaults/total -def bestCurve(defaults, total, defaultRate): - x = np.linspace(0, 1, total) - - y1 = np.linspace(0, 1, defaults) - y2 = np.ones(total-defaults) - y3 = np.concatenate([y1,y2]) - return x, y3 - -x, best = bestCurve(defaults=defaults, total=total, defaultRate=defaultRate) -plt.plot(x, best) - - -plt.show() - -!ec - - -!split -===== Analysis of the credi card example =====