I'm new to machine learning and trying Sklearn for the first time. I have two dataframes, one with data to train a logistic regression model (with 10-fold cross-validation) and another one to predict classes ('0,1') using that model.
Here's my code so far using bits of tutorials I found on Sklearn docs and on the Web:
import pandas as pd
import numpy as np
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.preprocessing import normalize
from sklearn.preprocessing import scale
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn import metrics
# Import dataframe with training data
df = pd.read_csv('summary_44.csv')
cols = df.columns.drop('num_class') # Data to use (num_class is the column with the classes)
# Import dataframe with data to predict
df_pred = pd.read_csv('new_predictions.csv')
# Scores
df_data = df.ix[:,:-1].values
# Target
df_target = df.ix[:,-1].values
# Values to predict
df_test = df_pred.ix[:,:-1].values
# Scores' names
df_data_names = cols.values
# Scaling
X, X_pred, y = scale(df_data), scale(df_test), df_target
# Define number of folds
kf = KFold(n_splits=10)
kf.get_n_splits(X) # returns the number of splitting iterations in the cross-validator
# Logistic regression normalizing variables
LogReg = LogisticRegression()
# 10-fold cross-validation
scores = [LogReg.fit(X[train], y[train]).score(X[test], y[test]) for train, test in kf.split(X)]
print scores
# Predict new
novel = LogReg.predict(X_pred)
Is this the correct way to implement a Logistic Regression?
I know that the fit() method should be used after cross-validation in order to train the model and use it for predictions. However, since I called fit() inside a list comprehension I really don't know if my model was "fitted" and can be used to make predictions.
I general things are okay, but there are some problems.
Scaling
X, X_pred, y = scale(df_data), scale(df_test), df_target
You scale training and test data independently, which isn't correct. Both datasets must be scaled with the same scaler. "Scale" is a simple function, but it is better to use something else, for example StandardScaler.
scaler = StandardScaler()
scaler.fit(df_data)
X = scaler.transform(df_data)
X_pred = scaler.transform(df_test)
Cross-validation and predicting.
How your code works? You split data 10 times into train and hold-out set; 10 times fit model on train set and calculate score on hold-out set. This way you get cross-validation scores, but the model is fitted only on a part of data. So it would be better to fit model on the whole dataset and then make a prediction:
LogReg.fit(X, y)
novel = LogReg.predict(X_pred)
I want to notice that there are advanced technics like stacking and boosting, but if you learn using sklearn, then it is better to stick to the basics.
Related
I am trying to run an artificial neural network with scikit-learn.
I want to run the regression, get the model fit results, an generate out of sample forecasts.
This is my code below. Any help will be greatly appreciated.
# Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPRegressor
#import the data
df=pd.read_excel(r"C:\Users\Action\Downloads\Python\Practice_Data\sorted_data v2.xlsx")
#view the data
df.head(5)
#to drop a column of data type
df2=df.drop('Unnamed: 13', axis=1)
#view the data
df2.head(5)
Import necessary modules
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from math import sqrt
from sklearn.metrics import r2_score
describe the data
df.describe().transpose()
target_column = ['public health care services']
predictors = list(set(list(df.columns))-set(target_column))
df[predictors] = df[predictors]/df[predictors].max()
df.describe().transpose()
set the X and Y
X = df[predictors].values
y = df[target_column].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=40)
print(X_train.shape); print(X_test.shape)
import MLP Classifier and fit the network
from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier(hidden_layer_sizes=(8,8,8), activation='relu', solver='adam', max_iter=500)
mlp.fit(X_train,y_train)
predict_train = mlp.predict(X_train)
set up the MLP Classifier
mlp = MLPClassifier(
hidden_layer_sizes=(50, 8),
max_iter=15,
alpha=1e-4,
solver="sgd",
verbose=True,
random_state=1,
learning_rate_init=0.1)
import the warnings
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings("ignore", category=ConvergenceWarning, module="sklearn")
predict_test = mlp.predict(X_test)
to train on the data I use the MLPClassifier to call the fit function on the training data.
mlp.fit(X_train, y_train)
after this, the neural network is done training.
after the neural network is trained, the next step is to test it.
print out the model scores
print(f"Training set score: {mlp.score(X_train, y_train)}")
print(f"Test set score: {mlp.score(X_test, y_test)}")
y_predict = mlp.predict(X_train)
I am getting an error from below
x_ann = y_predict[:, 0]
y_ann = y_predict[:, 1]
The error message is
IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
any help will be greatly appreciated
predict function gives you the actual class and since your point can belong to one and only one class (except multi label), it is supposed to be like this only
What is the shape of your Y_true_labels? Might be the case that your labels are Sparse and with 2 classes, means 0,1 and since the models is minimising Log Loss as described here as:
This model optimizes the log-loss function using LBFGS or stochastic gradient descent.
Also looking at the predict() it says:
log_y_probndarray of shape (n_samples, n_classes)
The predicted log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_. Equivalent to log(predict_proba(X))
So it means that if probability is 0.3 it means it belongs to class and if it's 0.7 it belongs to class, ASSUMING it's binary classification with a threshold set to 0.5.
What you might be confusing with is the predict_proba() function which gives you the probabilities for each classes.
Might be the case. Please post your X,Y data shape and type so that we can understand better.
I'm currently using cross_val_score and KFold to assess the impact of using StandardScaler at different points within data pre-processing, specifically whether scaling the entire training dataset prior to performing cross validation introduces data leakage and what the effect of this is when compared to scaling the data from within a Pipeline (and therefore only applying it to the training folds).
my current process is as follows:
Experiment A
Import the boston housing dataset from sklearn.datasets and split into Data (X) and target (y)
create a Pipeline (sklearn.pipeline), that applies StandardScaler before applying linear regression
Specify the cross validation method as KFold with 5 folds
Perform cross validation (cross_val_score) using the above Pipeline and KFold method and observe the score
Experiment B
Use the same boston housing data as above
fit_transform StandardScaler on the entire dataset
Use cross_val_Score to perform cross validation on again 5 folds but this time input LinearRegression directly rather than a pipeline
Compare the scores here to Experiment A
The scores obtained are identical (to around 13 decimal places) which I question as surely Experiment B introduces Data Leakage during cross validation.
I've seen posts stating that it doesnt matter whether scaling is done on the entire training set before cross validation, if this is true I'm looking to understand why, if this isn't true I'd like to understand why the scores can still be so similar despite the data leakage?
See my test code below:
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.linear_model import LinearRegression
np.set_printoptions(15)
boston = datasets.load_boston()
X = boston["data"]
y = boston["target"]
scalar = StandardScaler()
clf = LinearRegression()
class StScaler(StandardScaler):
def fit_transform(self,X,y=None):
print('Length of Data on which scaler is fit on =', len(X))
output = super().fit(X,y)
# print('mean of scalar =',output.mean_)
output = super().transform(X)
return output
pipeline = Pipeline([('sc', StScaler()), ('estimator', clf)])
cv = KFold(n_splits=5, random_state=42)
cross_val_score(pipeline, X, y, cv = cv)
# Now fitting Scaler on whole train data
scaler_2 = StandardScaler()
clf_2 = LinearRegression()
X_ss = scaler_2.fit_transform(X)
cross_val_score(clf_2, X_ss, y, cv=cv)
Thanks!
I have the following code so far:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn import preprocessing
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
df_train = pd.read_csv('uc_data_train.csv')
del df_train['Unnamed: 0']
temp = df_train['size_womenswear']
del df_train['size_womenswear']
df_train['size_womenswear'] = temp
df_train['count'] = 1
print(df_train.head())
print(df_train.dtypes)
print(df_train[['size_womenswear', 'count']].groupby('size_womenswear').count()) # Determine number of unique catagories, and number of cases for each catagory
del df_train['count']
df_test = pd.read_csv('uc_data_test.csv')
del df_test['Unnamed: 0']
print(df_test.head())
print(df_test.dtypes)
df_train.drop(['customer_id','socioeconomic_status','brand','socioeconomic_desc','order_method',
'first_order_channel','days_since_first_order','total_number_of_orders', 'return_rate'], axis=1, inplace=True)
LE = preprocessing.LabelEncoder() # Create label encoder
df_train['size_womenswear'] = LE.fit_transform(np.ravel(df_train[['size_womenswear']]))
print(df_train.head())
print(df_train.dtypes)
x = df_train.iloc[:,np.arange(len(df_train.columns)-1)].values # Assign independent values
y = df_train.iloc[:,-1].values # and dependent values
xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.25, random_state = 0) # Testing on 75% of the data
model = GaussianNB()
model.fit(xTrain, yTrain)
yPredicted = model.predict(xTest)
#print(yPrediction)
print('Accuracy: ', accuracy_score(yTest, yPredicted))
I am not sure how to include the data that I am using but I am trying to predict the 'size_womenswear'. There are 8 different sizes that I have encoded to predict and I have moved this column to the end of the dataframe. so y is the dependent and x are the independent (all the other columns)
I am using a Gaussian Naive Bayes classifier to try and classify the 8 different sizes and then test on 25% of the data. The results are not very good.
I don't know why I am only getting an accuracy of 61% when I am working with 80,000 rows. I am very new to Machine Learning and would appreciate any assistance. Is there a better method that I could use in this case than Gaussian Naive Bayes?
can't comment, just throwing out some ideas;
Maybe you need to deal with class imbalance, and try other model that will fit the data better? try the xgboost or lightgbm package given good data they usually perform pretty good in general, but it really depends on the data.
Also the way you split train and test, does the resulting train and test data set has similar distribution for your Y? that's very important.
Last thing, for classification models the performance measurement can be a bit tricky, try some other measurement methods. F1 scores or try to draw a confusion matrix and see what your predictions vs Y looks like. perhaps your model is predicting everything to one
or just a few classes.
I'm trying to recover the feature importance of a multioutput Classifier using a RandomForest.
The MultiOutput model does not show any problems:
import numpy as np
import pandas as pd
import sklearn
from sklearn.datasets import make_multilabel_classification
from sklearn.datasets import make_classification
from sklearn.multioutput import MultiOutputClassifier
from sklearn.ensemble import RandomForestClassifier
## Generate data
x, y = make_multilabel_classification(n_samples=1000,
n_features=15,
n_labels = 5,
n_classes=3,
random_state=12,
allow_unlabeled = True)
x_train = x[:700,:]
x_test = x[701:,:]
y_train = y[:700,:]
y_test = y[701:,:]
## Generate model
forest = RandomForestClassifier(n_estimators = 100, random_state = 1)
multi_forest = MultiOutputClassifier(forest, n_jobs = -1).fit(x_train, y_train)
## Make prediction
dfOutput_multi_forest = multi_forest.predict_proba(x_test)
The prediction dfOutput_multi_forest does not show any problems, but I want to recover the feature importance of the multi_forest for interpretation of the output.
Using multi_forest.feature_importance_ throws the following error message:
AttributeError: 'MultiOutputClassifier' object has no attribute 'feature_importance_'
Does anyone know how to retrieve the feature importance?
I'm using scikit v0.20.2
Indeed, it doesn't appear that Sklearn's MultiOutputClassifier has an attribute that contains some sort of amalgamation of the feature importances of all the estimators (in your case, all the RandomForest classifiers) used in the model.
However, it is possible to access the feature importances of each RandomForest classifier, and then average them all together to give you each feature's average importance, across all RandomForest classifiers.
MultiOutputClassifier objects have an attribute called estimators_. If you run multi_forest.estimators_, you will get a list containing an object for each of your RandomForest classifiers.
For each of these RandomForest classifier objects, you can access its feature importances through the feature_importances_ attribute.
To put it all together, this was my approach:
feat_impts = []
for clf in multi_forest.estimators_:
feat_impts.append(clf.feature_importances_)
np.mean(feat_impts, axis=0)
I ran the example code you pasted into your question, and then ran the above block of code to output a list of the following 15 averages:
array([0.09830467, 0.0912088 , 0.05738045, 0.1211305 , 0.03901933,
0.05429491, 0.06929378, 0.06404416, 0.05676634, 0.04919717,
0.05244265, 0.0509295 , 0.05615341, 0.09202444, 0.04780991])
Which contains the average importance of each of your 15 features, across each of the 3 random forest classifiers used in your MultiOutputClassifier.
This should at least help you to see which features, on the whole, tended to be more important in making predictions for each of your 3 classes.
I've just split my data into a training and testing set and my plan is to train a Linear Regression model and be able to check what the performance is like using my testing split.
My current code is:
import pandas as pd
import numpy as np
from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
df = pd.read_csv('C:/Dataset.csv')
df['split'] = np.random.randn(df.shape[0], 1)
split = np.random.rand(len(df)) <= 0.75
training_set = df[split]
testing_set = df[~split]
Is there a proper method I should be using to plot a Linear Regression model from an external file such as a .csv?
Since you want to use scikit-learn, here's an approach using sklearn.linear_model.LinearRegression:
from sklearn.linear_model import LinearRegression
model = LinearRegression()
X_train, y_train = training_set[x_vars], training_set[y_var]
X_test, y_test = testing_test[x_vars], testing_test[y_var]
model.fit(X_train, y_train)
predictions = model.predict(X_test)
Depending on whether you need more descriptive output, you might also look into use statsmodels for linear regression.