Let me keep this brief. Basically what I want to know is: should I do this,
pca.fit(normalize(x))
new=pca.transform(normalize(x))
or this
pca.fit(normalize(x))
new=pca.transform(x)
I know that we should normalize our data before using PCA but which one of the procedures above is correct with sklearn?
In general, you would want to use the first option.
Your normalization places your data in a new space which is seen by the PCA and its transform basically expects the data to be in the same space.
Scikit-learn provides tools to do this transparently and conveniently by concatenating estimators in a pipeline. Try:
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
import numpy as np
data = np.random.randn(20, 40)
pipeline = Pipeline([('scaling', StandardScaler()), ('pca', PCA(n_components=5))])
pipeline.fit_transform(data)
The prepended scaler will then always apply its transformation to the data before it goes to the PCA object.
As #larsmans points out, you may want to use sklearn.preprocessing.Normalizer instead of the StandardScaler or, similarly, remove the mean centering from the StandardScaler by passing the keyword argument with_mean=False.
Related
So after a year of arduous work, my model is finally being implemented in my company's productive servers.
In this productive environment, my model is loaded in a Python script and a string is pulled from another server. I now have to parse this string and pass it to the model so it can make a prediction and return that output to the end user.
My current concern is efficiency. I am looking for a very fast way to convert the string to an array-like object that can be passed to my model.
Here's a replicable example:
# Load modules
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import GradientBoostingClassifier
# Load dummy data and target
X = load_breast_cancer()['data']
y = load_breast_cancer()['target']
# Initialize and fit classifier
clf = GradientBoostingClassifier(random_state=0)
clf.fit(X, y)
# [1] New string is received
string = '17.99|10.38|122.8|1001.0|0.1184|0.2776|0.3001|0.1471|0.2419|0.07871|1.095|0.9053|8.589|153.4|0.006399|0.04904|0.05373|0.01587|0.03003|0.006193|25.38|17.33|184.6|2019.0|0.1622|0.6656|0.7119|0.2654|0.4601|0.1189'
# [2] Convert string to array-like structure
import numpy as np
x = np.array(string.split('|')).astype(float)
# [3] Pass `x` to `clf` and predict probability
clf.predict_proba(x.reshape(-1, 30)).item(0)
> 0.9987537665581022
My question
Is there a more efficient way to parse a string and pass it to an sklearn model?
I think skipping the import numpy would speed things up. However, I'm open to any solution that can improve the runtime of steps [1], [2] and [3].
make sure that you indeed need double precision
and use
fromstring = np.fromstring
# ...
fromstring(string, 'f', -1, '|')
it will be 3-4x faster than
np.array(string.split('|')).astype(float)
I am trying to create an AI that reads my dataset and states whether an input outside the data is 1 or 0
My dataset has column for qualitative data and column for a boolean. Here is a sample from it:
Imports:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
import re
import string
Open and cleaning dataset:
saisei_data = saisei_data.dropna(how='any',axis=0)
saisei_data = saisei_data.sample(frac=1)
X = saisei_data['Data']
y = saisei_data['Conscious']
saisei_data
Vectorisation:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorization = TfidfVectorizer()
xv_train = vectorization.fit_transform(X_train)
xv_test = vectorization.fit_transform(X_test)
Example Algorithm - Logistic Regression:
LR = LogisticRegression()
LR.fit(xv_train,y_train)
pred_lr=LR.predict(xv_test) # Here is where I get an error
Everything works fine until I predict using the logistic regression algorithm.
The Error:
ValueError: X has 112 features per sample; expecting 23
This seems to change to similar errors such as:
ValueError: X has 92 features per sample; expecting 45
I am new to machine learning so I don't really know what I'm doing when it comes to using the algorithms, however I tried printing the xv_test variable and here is a sample of the output (also changes often):
Any ideas?
That is because you erroneously apply .fit_transform() to your test data; and, in this case, you are lucky enough that the process produces a programming error, thus alerting you that you are doing something methodologically wrong (which is not always the case).
We never apply either .fit() or .fit_transform() to unseen (test) data. The fitting should be done only once with the training data, like you have done here:
xv_train = vectorization.fit_transform(X_train)
For subsequent transformations of unseen (test) data, we use only .transform(). So, your next line should be
xv_test = vectorization.transform(X_test)
That way, the features in the test set will be the same with the ones in the training set, as it should be in the first place.
Notice the difference between the two methods in the docs (emphasis mine):
fit_transform:
Learn vocabulary and idf, return document-term matrix.
transform:
Transform documents to document-term matrix.
Uses the vocabulary and document frequencies (df) learned by fit (or fit_transform).
and recall that we don't ever use the test set to learn anything.
So, simple general mnemonic rule, applicable practically everywhere:
The terms "fit" and "test data" are always (always...) incompatible; mixing them will create havoc.
I'm relatively new to Python and am trying to get some data prepped to train a RandomForest. For various reasons, we want the data to be discrete, so there are a few continuous variables that need to be discretized. I found qcut in pandas, which seems to do what I want - I can set a number of bins, and it will discretize the variable into that many bins, trying to keep the counts in each bin even.
However, the output of pandas.qcut is a list of Intervals, and the RandomForest classifier in scikit-learn needs a string. I found that I can convert an interval into a string by using .astype(str). Here's a quick example of what I'm doing:
import pandas as pd
from random import sample
vals = sample(range(0,100), 100)
cuts = pd.qcut(vals, q=5)
str_cuts = pd.qcut(vals, q=5).astype(str)
and then str_cuts is one of the variables passed into a random forest.
However, the intent of this system is to train a RandomForest, save it to a file, and then allow someone to load it at a later date and get a classification for a new test instance, that is not available at training time. And because the classifier was trained on discretized data, the new test instance will need to be discretized before it can be used. So what I want to be able to do is read in a new instance, apply the already-established discretization scheme to it, convert it to a string, and run it through the random forest. However, I'm getting hung up on the best way to 'apply the discretization scheme'.
Is there an easy way to handle this? I assume there's no straight-forward way to convert a string back into an Interval. I can get the list of all Interval values from the discretization (ex: cuts.unique()) and apply that at test-time, but that would require saving/loading a discretization dictionary alongside the random forest, which seems clunky, and I worry about running into issues trying to recreate a categorical variable (coming mostly from R, which is extremely particular about the format of categorical variables). Or is there another way around this that I'm not seeing?
Use the labelsargument in qcut and use pandas Categorical.
Either of those can help you create categories instead of interval for your variable. Then, you can use a form of encoding, for example Label Encoding or Ordinal Encoding to convert the categories (the factors if you're used to R) to numerical values which the Forest will be able to use.
Then the process goes :
cutting => categoricals => encoding
and you don't need to do it by hand anymore.
Lastly, some gradient boosted trees libraries have support for categorical variables though it's not a silver bullet and will depend on your goal. See catboost and lightgbm.
For future searchers, there are benefits to using transformers from scikit-learn instead of pandas. In this case, KBinsDiscretizer is the scikit equivalent of qcut.
It can be used in a pipeline, which will handle applying the previously-learned discretization to unseen data without the need for storing the discretization dictionary separately or round trip string conversion. Here's an example:
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import KBinsDiscretizer
pipeline = make_pipeline(KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='quantile'),
RandomForestClassifier())
X, y = make_classification()
X_train, X_test, y_train, y_test = train_test_split(X, y)
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
If you really need to convert back and forth between pandas IntervalIndex and string, you'll probably need to do some parsing as described in this answer: https://stackoverflow.com/a/65296110/3945991 and either use FunctionTransformer or write your own Transformer for pipeline integration.
While it may not be the cleanest-looking method, converting a string back into an interval is indeed possible:
import pandas as pd
str_intervals = [i.replace("(","").replace("]", "").split(", ") for i in str_cuts]
original_cuts = [pd.Interval(float(i), float(j)) for i, j in str_intervals]
First of all thanks in advance, I don't really know if I should open an issue so I wanted to check if someone had faced this before.
So I'm having the following problem when using a CalibratedClassifierCV for text classification. I have an estimator which is a pipeline created this way (simple example):
# Import libraries first
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import make_pipeline
from sklearn.calibration import CalibratedClassifierCV
from sklearn.linear_model import LogisticRegression
# Now create the estimators: pipeline -> calibratedclassifier(pipeline)
pipeline = make_pipeline( TfidfVectorizer(), LogisticRegression() )
calibrated_pipeline = CalibratedClassifierCV( pipeline, cv=2 )
Now we can create a simple train set to check if the classifier works:
# Create text and labels arrays
text_array = np.array(['Why', 'is', 'this', 'happening'])
outputs = np.array([0,1,0,1])
When I try to fit the calibrated_pipeline object, I get this error:
ValueError: Found input variables with inconsistent numbers of samples: [1, 4]
If you want I can copy the whole exception trace, but this should be easily reproducible. Thanks a lot in advance!
EDIT: I made a mistake when creating the arrays. Fixed now (Thanks #ogrisel !) Also, calling:
pipeline.fit(text_array, outputs)
works properly, but doing so with the calibrated classifier fails!
np.array(['Why', 'is', 'this', 'happening']).reshape(-1,1) is a 2D array of strings while the docstring of the fit_transform method of the TfidfVectorizer class states that it expects:
Parameters
----------
raw_documents : iterable
an iterable which yields either str, unicode or file objects
If you iterate over your 2D numpy array you get a sequence of 1D arrays of strings instead of strings directly:
>>> list(text_array)
[array(['Why'],
dtype='<U9'), array(['is'],
dtype='<U9'), array(['this'],
dtype='<U9'), array(['happening'],
dtype='<U9')]
So the fix is easy, just pass:
text_documents = ['Why', 'is', 'this', 'happening']
as the raw input to the vectorizer.
Edit: remark: LogisticRegression is almost always a well calibrated classifier by default. It will likely be the case that CalibratedClassifierCV won't bring anything in this case.
I am trying to apply a univariate feature selection method using the Python module scikit-learn to a regression (i.e. continuous valued response values) dataset in svmlight format.
I am working with scikit-learn version 0.11.
I have tried two approaches - the first of which failed and the second of which worked for my toy dataset but I believe would give meaningless results for a real dataset.
I would like advice regarding an appropriate univariate feature selection approach I could apply to select the top N features for a regression dataset. I would either like (a) to work out how to make the f_regression function work or (b) to hear alternative suggestions.
The two approaches mentioned above:
I tried using sklearn.feature_selection.f_regression(X,Y).
This failed with the following error message:
"TypeError: copy() takes exactly 1 argument (2 given)"
I tried using chi2(X,Y). This "worked" but I suspect this is because the two response values 0.1 and 1.8 in my toy dataset were being treated as class labels? Presumably, this would not yield a meaningful chi-squared statistic for a real dataset for which there would be a large number of possible response values and the number in each cell [with a particular response value and value for the attribute being tested] would be low?
Please find my toy dataset pasted into the end of this message.
The following code snippet should give the results I describe above.
from sklearn.datasets import load_svmlight_file
X_train_data, Y_train_data = load_svmlight_file(svmlight_format_train_file) #i.e. change this to the name of my toy dataset file
from sklearn.feature_selection import SelectKBest
featureSelector = SelectKBest(score_func="one of the two functions I refer to above",k=2) #sorry, I hope this message is clear
featureSelector.fit(X_train_data,Y_train_data)
print [1+zero_based_index for zero_based_index in list(featureSelector.get_support(indices=True))] #This should print the indices of the top 2 features
Thanks in advance.
Richard
Contents of my contrived svmlight file - with additional blank lines inserted for clarity:
1.8 1:1.000000 2:1.000000 4:1.000000 6:1.000000#mA
1.8 1:1.000000 2:1.000000#mB
0.1 5:1.000000#mC
1.8 1:1.000000 2:1.000000#mD
0.1 3:1.000000 4:1.000000#mE
0.1 3:1.000000#mF
1.8 2:1.000000 4:1.000000 5:1.000000 6:1.000000#mG
1.8 2:1.000000#mH
As larsmans noted, chi2 cannot be used for feature selection with regression data.
Upon updating to scikit-learn version 0.13, the following code selected the top two features (according to the f_regression test) for the toy dataset described above.
def f_regression(X,Y):
import sklearn
return sklearn.feature_selection.f_regression(X,Y,center=False) #center=True (the default) would not work ("ValueError: center=True only allowed for dense data") but should presumably work in general
from sklearn.datasets import load_svmlight_file
X_train_data, Y_train_data = load_svmlight_file(svmlight_format_train_file) #i.e. change this to the name of my toy dataset file
from sklearn.feature_selection import SelectKBest
featureSelector = SelectKBest(score_func=f_regression,k=2)
featureSelector.fit(X_train_data,Y_train_data)
print [1+zero_based_index for zero_based_index in list(featureSelector.get_support(indices=True))]
You could also try to do feature selection by L1/Lasso regularization. The class specifically designed for this is RandomizedLasso which will train LassoRegression on multiple subsamples of your data and select features that are selected most frequently by these models. You can also just use Lasso, LassoLars or SGDClassifier to do same thing without the benefit of resampling but faster.