Develop Reference

Can GridSearchCV be used for unsupervised learning?

I am trying to build an outlier detector to find outliers in test data. That data varies a bit (more test channels, longer testing).
First im applying the train test split because i wanted to use grid search with train data to get the best results. This is timeseries data from multiple sensors and i removed the time column beforehand.
X shape : (25433, 17)
y shape : (25433, 1)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=(0))
Standardize afterwards and then i changed them into an int Array because GridSearch doesnt seem to like continuous data. This surely can be done better, but i want this to work before i optimize the coding.
'X'
mean = StandardScaler().fit(X_train)
X_train = mean.transform(X_train)
X_test = mean.transform(X_test)
X_train = np.round(X_train,2)*100
X_train = X_train.astype(int)
X_test = np.round(X_test,2)*100
X_test = X_test.astype(int)
'y'
yeah = StandardScaler().fit(y_train)
y_train = yeah.transform(y_train)
y_test = yeah.transform(y_test)
y_train = np.round(y_train,2)*100
y_train = y_train.astype(int)
y_test = np.round(y_test,2)*100
y_test = y_test.astype(int)
I chose the IsoForrest because its fast, has pretty good results and can handle huge data sets (i currently only use a chunk of the data for testing).
SVM might also be an option i want to check out.
Then i set up the GridSearchCV
clf = IForest(random_state=47, behaviour='new',
n_jobs=-1)
param_grid = {'n_estimators': [20,40,70,100],
'max_samples': [10,20,40,60],
'contamination': [0.1, 0.01, 0.001],
'max_features': [5,15,30],
'bootstrap': [True, False]}
fbeta = make_scorer(fbeta_score,
average = 'micro',
needs_proba=True,
beta=1)
grid_estimator = model_selection.GridSearchCV(clf,
param_grid,
scoring=fbeta,
cv=5,
n_jobs=-1,
return_train_score=True,
error_score='raise',
verbose=3)
grid_estimator.fit(X_train, y_train)
The Problem:
GridSearchCV needs an y argument, so i think this only works with supervised learning? If i run this i get the following error that i dont understand:
ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets

You can use GridSearchCV for unsupervised learning, but it's often tricky to define a scoring metric that makes sense for the problem.
Here's an example in the docs that uses grid search for KernelDensity, an unsupervised estimator. It works without issue because this estimator has a score method (docs).
In your case, since IsolationForest doesn't have a score method, you'll need to define a custom scorer to pass as the search's scoring method. There's an answer at this question, and also this question, but I don't think the metrics given there necessarily makes sense. Unfortunately, I don't have a useful outlier detection metric in mind; that's a question better suited for the data science or statistics stackexchange sites.

Agree with #Ben Reiniger's answer and it has good links for other SO posts on this topic.
You can try creating a custom scorer by assuming you can make use of y_train . This is not strictly unsupervised .
Here is one example where R2 score is used as a scoring metric.
from sklearn.metrics import r2_score
def scorer_f(estimator, X_train,Y_train):
y_pred=estimator.predict(Xtrain)
return r2_score(Y_train, y_pred)
Then you can use it as normal.
clf = IForest(random_state=47, behaviour='new',
n_jobs=-1)
param_grid = {'n_estimators': [20,40,70,100],
'max_samples': [10,20,40,60],
'contamination': [0.1, 0.01, 0.001],
'max_features': [5,15,30],
'bootstrap': [True, False]}
grid_estimator = model_selection.GridSearchCV(clf,
param_grid,
scoring=scorer_f,
cv=5,
n_jobs=-1,
return_train_score=True,
error_score='raise',
verbose=3)
grid_estimator.fit(X_train, y_train)

GridSearchCV returns worse accuracy than default

I was working with the Heart Disease Prediction dataset from Kaggle and came up with something odd that I couldn't find an answer.
With default Logistic Regression with 'liblinear' solver (C = 1) I get an accuracy on the train set of 87.26% and 86.81% on the test set. Pretty good. However, I tried using GridSearchCV tweaking C in case I find better values and I constantly get worse results (an accuracy about 85% on train set and 82.5% on test set).
Is GridSearchCV using some other metric for comparing these values of C? I just don't understand why it returns a worse solution.
I leave the last part of my code here.
Default Logistic Regression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(solver = 'liblinear', random_state = 2)
lr = lr.fit(X_train, y_train)
model_score(lr, X_train, y_train, X_test, y_test)
GridSearchCV
from sklearn.model_selection import GridSearchCV
# Finding better hyperparameters for the Logistic Regression
lr_params = [ {'C': np.logspace(-1, 0.3, 30)} ]
lr = LogisticRegression(solver = 'liblinear', random_state = 2)
lr_cv = GridSearchCV(lr, lr_params, cv = 5, scoring = 'accuracy')
lr_cv.fit(X_train, y_train)
lr_best_params = lr_cv.best_params_
lr = LogisticRegression(**lr_best_params)
lr.fit(X_train, y_train)
model_score(lr, X_train, y_train, X_test, y_test)
EDIT
Full code in this link (check section 4-4.1).

Should Cross Validation Score be performed on original or split data?

When I want to evaluate my model with cross validation, should I perform cross validation on original (data thats not split on train and test) or on train / test data?
I know that training data is used for fitting the model, and testing for evaluating. If I use cross validation, should I still split the data into train and test, or not?
features = df.iloc[:,4:-1]
results = df.iloc[:,-1]
x_train, x_test, y_train, y_test = train_test_split(features, results, test_size=0.3, random_state=0)
clf = LogisticRegression()
model = clf.fit(x_train, y_train)
accuracy_test = cross_val_score(clf, x_test, y_test, cv = 5)
Or should I do like this:
features = df.iloc[:,4:-1]
results = df.iloc[:,-1]
clf = LogisticRegression()
model = clf.fit(features, results)
accuracy_test = cross_val_score(clf, features, results, cv = 5)), 2)
Or maybe something different?

Both your approaches are wrong.
In the first one, you apply cross validation to the test set, which is meaningless
In the second one, you first fit the model with your whole data, and then you perform cross validation, which is again meaningless. Moreover, the approach is redundant (your fitted clf is not used by the cross_val_score method, which does its own fitting)
Since you are not doing any hyperparameter tuning (i.e. you seem to be interested only in performance assessment), there are two ways:
Either with a separate test set
Or with cross validation
First way (test set):
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
x_train, x_test, y_train, y_test = train_test_split(features, results, test_size=0.3, random_state=0)
clf = LogisticRegression()
model = clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
accuracy_test = accuracy_score(y_test, y_pred)
Second way (cross validation):
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
clf = LogisticRegression()
# shuffle data first:
features_s, results_s = shuffle(features, results)
accuracy_cv = cross_val_score(clf, features_s, results_s, cv = 5, scoring='accuracy')
# fit the model afterwards with the whole data, if satisfied with the performance:
model = clf.fit(features, results)

I will try to summarize the "best practice" here:
1) If you want to train your model, fine-tune parameters, and do final evaluation, I recommend you to split your data into training|val|test.
You fit your model using the training part, and then you check different parameter combinations on the val part. Finally, when you're sure which classifier/parameter obtains the best result on the val part, you evaluate on the test to get the final rest.
Once you evaluate on the test part, you shouldn't change the parameters any more.
2) On the other hand, some people follow another way, they split their data into training and test, and they finetune their model using cross-validation on the training part and at the end they evaluate it on the test part.
If your data is quite large, I recommend you to use the first way, but if your data is small, the 2.

Sklearn GridSearch with PredefinedSplit scoring does not match a standalone classifier

I am using sklearn GridSearch to find best parameters for random forest classification using a predefined validation set. The scores from the best estimator returned by GridSearch do not match the scores obtained by training a separate classifier with the same parameters.
The data split definition
X = pd.concat([X_train, X_devel])
y = pd.concat([y_train, y_devel])
test_fold = -X.index.str.contains('train').astype(int)
ps = PredefinedSplit(test_fold)
The GridSearch definition
n_estimators = [10]
max_depth = [4]
grid = {'n_estimators': n_estimators, 'max_depth': max_depth}
rf = RandomForestClassifier(random_state=0)
rf_grid = GridSearchCV(estimator = rf, param_grid = grid, cv = ps, scoring='recall_macro')
rf_grid.fit(X, y)
The classifier definition
clf = RandomForestClassifier(n_estimators=10, max_depth=4, random_state=0)
clf.fit(X_train, y_train)
The recall was calculated explicitly using sklearn.metrics.recall_score
y_pred_train = clf.predict(X_train)
y_pred_devel = clf.predict(X_devel)
uar_train = recall_score(y_train, y_pred_train, average='macro')
uar_devel = recall_score(y_devel, y_pred_devel, average='macro')
GridSearch
uar train: 0.32189884516029466
uar devel: 0.3328299259976279
Random Forest:
uar train: 0.483040291148839
uar devel: 0.40706644557392435
What is the reason for such a mismatch?

There are multiple issues here:
Your input arguments to recall_score are reversed. The actual correct order is:
recall_score(y_true, y_test)
But you are are doing:
recall_score(y_pred_train, y_train, average='macro')
Correct that to:
recall_score(y_train, y_pred_train, average='macro')
You are doing rf_grid.fit(X, y) for grid-search. That means that after finding the best parameter combinations, the GridSearchCV will fit the whole data (whole X, ignoring the PredefinedSplit because that's only used during cross-validation in search of best parameters). So in essence, the estimator from GridSearchCV will have seen the whole data, so scores will be different from what you get when you do clf.fit(X_train, y_train)

It's because in your GridSearchCV you are using the scoring function as recall-macro which basically return the recall score which is macro averaged. See this link.
However, when you are returning the default score from your RandomForestClassifier it returns the mean accuracy. So, that is why the scores are different. See this link for info on the same. (Since one is recall and the other is accuracy).

How to apply StandardScaler in Pipeline in scikit-learn (sklearn)?

In the example below,
pipe = Pipeline([
('scale', StandardScaler()),
('reduce_dims', PCA(n_components=4)),
('clf', SVC(kernel = 'linear', C = 1))])
param_grid = dict(reduce_dims__n_components=[4,6,8],
clf__C=np.logspace(-4, 1, 6),
clf__kernel=['rbf','linear'])
grid = GridSearchCV(pipe, param_grid=param_grid, cv=3, n_jobs=1, verbose=2)
grid.fit(X_train, y_train)
print(grid.score(X_test, y_test))
I am using StandardScaler(), is this the correct way to apply it to test set as well?

Yes, this is the right way to do this but there is a small mistake in your code. Let me break this down for you.
When you use the StandardScaler as a step inside a Pipeline then scikit-learn will internally do the job for you.
What happens can be described as follows:
Step 0: The data are split into TRAINING data and TEST data according to the cv parameter that you specified in the GridSearchCV.
Step 1: the scaler is fitted on the TRAINING data
Step 2: the scaler transforms TRAINING data
Step 3: the models are fitted/trained using the transformed TRAINING data
Step 4: the scaler is used to transform the TEST data
Step 5: the trained models predict using the transformed TEST data
Note: You should be using grid.fit(X, y) and NOT grid.fit(X_train, y_train) because the GridSearchCV will automatically split the data into training and testing data (this happen internally).
Use something like this:
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.decomposition import PCA
pipe = Pipeline([
('scale', StandardScaler()),
('reduce_dims', PCA(n_components=4)),
('clf', SVC(kernel = 'linear', C = 1))])
param_grid = dict(reduce_dims__n_components=[4,6,8],
clf__C=np.logspace(-4, 1, 6),
clf__kernel=['rbf','linear'])
grid = GridSearchCV(pipe, param_grid=param_grid, cv=3, n_jobs=1, verbose=2, scoring= 'accuracy')
grid.fit(X, y)
print(grid.best_score_)
print(grid.cv_results_)
Once you run this code (when you call grid.fit(X, y)), you can access the outcome of the grid search in the result object returned from grid.fit(). The best_score_ member provides access to the best score observed during the optimization procedure and the best_params_ describes the combination of parameters that achieved the best results.
IMPORTANT EDIT 1: if you want to keep a validation dataset of the original dataset use this:
X_for_gridsearch, X_future_validation, y_for_gridsearch, y_future_validation
= train_test_split(X, y, test_size=0.15, random_state=1)
Then use:
grid = GridSearchCV(pipe, param_grid=param_grid, cv=3, n_jobs=1, verbose=2, scoring= 'accuracy')
grid.fit(X_for_gridsearch, y_for_gridsearch)

Quick answer: Your methodology is correct.
Although the above answer is very good, I just would like to point out some subtleties:
best_score_ [1] is the best cross-validation metric, and not the generalization performance of the model [2]. To evaluate how well the best found parameters generalize, you should call the score on the test set, as you've done. Therefore it is needed to start by splitting the data into training and test set, fit the grid search only in the X_train, y_train, and then score it with X_test, y_test [2].
Deep Dive:
A threefold split of data into training set, validation set and test set is one way to prevent overfitting in the parameters during grid search. On the other hand, GridSearchCV uses Cross-Validation in the training set, instead of having both training and validation set, but this does not replace the test set. This can be verified in [2] and [3].
References:
[1] GridSearchCV
[2] Introduction to Machine Learning with Python
[3] 3.1 Cross-validation: evaluating estimator performance