I have written the following code to generate a confusion matrix
from sklearn.naive_bayes import MultinomialNB
mnb=MultinomialNB()
mnb.fit(X_train,Y_train)
sms2="REMINDER FROM O2: To get 2.50 pounds free call credit and details of great offers pls reply 2 this text with your valid name, house no and postcode"
sms="You’ve Won!"
X_test = [str (item) for item in X_test]
Y_pred = mnb.predict(vec.transform(X_test))
from sklearn.metrics import confusion_matrix
mat = confusion_matrix(Y_test, Y_pred)
print(mat)
names =[ "non-spam", "spam"]
print(names)
sns.heatmap(mat, square=True, annot=True, fmt='d', cbar=False,
xticklabels=names, yticklabels=names)
plt.xlabel('Actual [Truth]')
plt.ylabel('Predicted')
plt.show()
It generated the following confusion matrix:
I am not sure if the axis are labelled correctly
ie. If the x-axis should be Actual and y-axis should be predicted
or the other way around
According to the scikit=learn documentation, https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html, the first call parameter should be the ground truth values, and the second call parameter should be the predicted values via the classifier.
If that is the case, then the return parameter for the confusion matrix should have the rows containing the true classes and the columns should be the predicted classes.
I think your labels are backward based on that. See also the example https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_display_object_visualization.html#sphx-glr-auto-examples-miscellaneous-plot-display-object-visualization-py
Related
My LightGBM regressor model returns negative values.
For XGBoost there is objective='count:poisson' hyperparameter in order to prevent returning negative predicitons.
Is there any chance to do this ?
Github issue => https://github.com/microsoft/LightGBM/issues/5629
LightGBM also supports poisson regression. For example, consider the following Python code.
import lightgbm as lgb
import numpy as np
from matplotlib import pyplot
# random Poisson-distributed target and one informative feature
y = np.random.poisson(lam=15.0, size=1_000)
X = y + np.random.normal(loc=10.0, scale=2.0, size=(y.shape[0], ))
X = X.reshape(-1, 1)
# fit a Poisson regression model
reg = lgb.LGBMRegressor(
objective="poisson",
n_estimators=150,
min_data=1
)
reg.fit(X, y)
# get predictions
preds = reg.predict(X)
print("summary of predicted values")
print(f" * min: {round(np.min(preds), 3)}")
print(f" * max: {round(np.max(preds), 3)}")
# compare predicted distribution to the empirical one
bins = np.linspace(0, 30, 50)
pyplot.hist(y, bins, alpha=0.5, label='actual')
pyplot.hist(preds, bins, alpha=0.5, label='predicted')
pyplot.legend(loc='upper right')
pyplot.show()
This example uses Python 3.10 and lightgbm==3.3.3.
However... I don't recommend using Poisson regression just to achieve "no negative predictions". The Poisson loss function is intended to be used for cases where you believe your target is Poisson-distributed, e.g. it looks like counts of events observed over some regular interval like time or space.
Other options you might consider to try to achieve the behavior "never predict a negative number from LightGBM regression":
write a custom objective function in one of the interfaces that support it, like the R or Python package
post-process LightGBM's predictions, recoding negative values to 0
pre-process the target variable such that there are no negative values (e.g. dropping such observations, re-scaling, taking the absolute value)
LightGBM also facilitates an objective parameter which can be set to 'poisson'. Follow this link for more information.
An example for LGBMRegressor (scikit-learn API):
from lightgbm import LGBMRegressor
regressor = LGBMRegressor(objective='poisson')
I have developed a Random Forest model which is including two inputs as X and one output as Y. I have normalized both X and Y values for the training process.
After the model get trained, I selected the dataset as an unseen data for an input for the model. The data is coming from another resource. I normalized the X values and imported them to the trained model and get the Y-normalized value as an output. I wonder how the de normalizing process would be. I mean I have to multiply the output by which value to get the denormalized value?
I'd appreciate it if someone can help me in this regard.
You need to do the prepossessing inversely. But, you the mean and sd (standard deviation) values that used for normalization.
For example with scikit learn you can do it easily. You can do it with 1 line of code.
enter code here
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data= ...
scaled_data = scaler.fit_transform(data)
inverse = scaler.inverse_transform(scaled_data)
I'm a beginner and making a linear regression model, when I make predictions on the basis of test sets, it works fine. But when I try to predict something for a specific value. It gives an error. The tutorial I'm watching, they don't have any errors.
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)
# Visualising the Linear Regression results
plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg.predict(X), color = 'blue')
plt.title('Truth or Bluff (Linear Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
# Predicting a new result with Linear Regression
lin_reg.predict(6.5)
ValueError: Expected 2D array, got scalar array instead:
array=6.5.
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
According to the Scikit-learn documentation, the input array should have shape (n_samples, n_features). As such, if you want a single example with a single value, you should expect the shape of your input to be (1,1).
This can be done by doing:
import numpy as np
test_X = np.array(6.5).reshape(-1, 1)
lin_reg.predict(test_X)
You can check the shape by doing:
test_X.shape
The reason for this is because the input can have many samples (i.e. you want to predict for multiple data points at once), or/and each sample can have many features.
Note: Numpy is a Python library to support large arrays and matrices. When scikit-learn is installed, Numpy should be installed as well.
Hi I was following the Machine Learning course by Andrew Ng.
I found that in regression problems, specially logistic regression they have used integer values for the features which could be plotted in a graph. But there are so many use cases where the feature values may not be integer.
Let's consider the follow example :
I want to build a model to predict if any particular person will take a leave today or not. From my historical data I may find the following features helpful to build the training set.
Name of the person, Day of the week, Number of Leaves left for him till now (which maybe a continuous decreasing variable), etc.
So here are the following questions based on above
How do I go about designing the training set for my logistic regression model.
In my training set, I find some variables are continuously decreasing (ex no of leaves left). Would that create any problem, because I know continuously increasing or decreasing variables are used in linear regression. Is that true ?
Any help is really appreciated. Thanks !
Well, there are a lot of missing information in your question, for example, it'll be very much clearer if you have provided all the features you have, but let me dare to throw some assumptions!
ML Modeling in classification always requires dealing with numerical inputs, and you can easily infer each of the unique input as an integer, especially the classes!
Now let me try to answer your questions:
How do I go about designing the training set for my logistic regression model.
How I see it, you have two options (not necessary both are practical, it's you who should decide according to the dataset you have and the problem), either you predict the probability of all employees in the company who will be off in a certain day according to the historical data you have (i.e. previous observations), in this case, each employee will represent a class (integer from 0 to the number of employees you want to include). Or you create a model for each employee, in this case the classes will be either off (i.e. Leave) or on (i.e. Present).
Example 1
I created a dataset example of 70 cases and 4 employees which looks like this:
Here each name is associated with the day and month they took as off with respect to how many Annual Leaves left for them!
The implementation (using Scikit-Learn) would be something like this (N.B date contains only day and month):
Now we can do something like this:
import math
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold
# read dataset example
df = pd.read_csv('leaves_dataset.csv')
# assign unique integer to every employee (i.e. a class label)
mapping = {'Jack': 0, 'Oliver': 1, 'Ruby': 2, 'Emily': 3}
df.replace(mapping, inplace=True)
y = np.array(df[['Name']]).reshape(-1)
X = np.array(df[['Leaves Left', 'Day', 'Month']])
# create the model
parameters = {'penalty': ['l1', 'l2'], 'C': [0.1, 0.5, 1.0, 10, 100, 1000]}
lr = LogisticRegression(random_state=0)
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=0)
clf = GridSearchCV(lr, parameters, cv=cv)
clf.fit(X, y)
#print(clf.best_estimator_)
#print(clf.best_score_)
# Example: probability of all employees who have 10 days left today
# warning: date must be same format
prob = clf.best_estimator_.predict_proba([[10, 9, 11]])
print({'Jack': prob[0,0], 'Oliver': prob[0,1], 'Ruby': prob[0,2], 'Emily': prob[0,3]})
Result
{'Ruby': 0.27545, 'Oliver': 0.15032,
'Emily': 0.28201, 'Jack': 0.29219}
N.B
To make this relatively work you need a real big dataset!
Also this can be better than the second one if there are other informative features in the dataset (e.g. the health status of the employee at that day..etc).
The second option is to create a model for each employee, here the result would be more accurate and more reliable, however, it's almost a nightmare if you have too many employees!
For each employee, you collect all their leaves in the past years and concatenate them into one file, in this case you have to complete all days in the year, in other words: for every day that employee has never got it off, that day should be labeled as on (or numerically speaking 1) and for the days off they should be labeled as off (or numerically speaking 0).
Obviously, in this case, the classes will be 0 and 1 (i.e. on and off) for each employee's model!
For example, consider this dataset example for the particular employee Jack:
Example 2
Then you can do for example:
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold
# read dataset example
df = pd.read_csv('leaves_dataset2.csv')
# assign unique integer to every on and off (i.e. a class label)
mapping = {'off': 0, 'on': 1}
df.replace(mapping, inplace=True)
y = np.array(df[['Type']]).reshape(-1)
X = np.array(df[['Leaves Left', 'Day', 'Month']])
# create the model
parameters = {'penalty': ['l1', 'l2'], 'C': [0.1, 0.5, 1.0, 10, 100, 1000]}
lr = LogisticRegression(random_state=0)
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=0)
clf = GridSearchCV(lr, parameters, cv=cv)
clf.fit(X, y)
#print(clf.best_estimator_)
#print(clf.best_score_)
# Example: probability of the employee "Jack" who has 10 days left today
prob = clf.best_estimator_.predict_proba([[10, 9, 11]])
print({'Off': prob[0,0], 'On': prob[0,1]})
Result
{'On': 0.33348, 'Off': 0.66651}
N.B in this case you have to create a dataset for each employee + training especial model + filling all the days the never taken in the past years as off!
In my training set, I find some variables are continuously decreasing (ex no of leaves left). Would that create any problem,
because I know continuously increasing or decreasing variables are
used in linear regression. Is that true ?
Well, there is nothing preventing you from using contentious values as features (e.g. number of leaves) in Logistic Regression; actually it doesn't make any difference if it's used in Linear or Logistic Regression but I believe you got confused between the features and the response:
The thing is, discrete values should be used in the response of Logistic Regression and Continuous values should be used in the response of the Linear Regression (a.k.a dependent variable or y).
I am new to statistics, Python, machine learning and Scikit-learn. However, I am trying this project where I have a CSV with 35 columns of student data. The first column is an ID which I think I can ignore. The last 3 columns are the grade 1, grade 2 and grade 3 scores. I have 400 rows. I want to see if I can learn some machine learning with it, and make some sense of the data I have. Now I understand Scikit works on Numpy arrays which do not handle categorical data like sex ('male', 'female') and so on. So I codified all the 30 categories with 1 for male, 2 for female and so on and so forth. I then did the following
X = my_data[:,1:33]
y = my_data[:,34]
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X,y)
expected = y
predicted = model.predict(X)
mse = np.mean((predicted-expected)**2)
print(mse)
print(model.score(X,y))
I got a MSE of 6.0839840461 and a model score of 0.709407474898.
I got some result. So far so good for a first attempt. However, I realized that since I assigned increasing code values like 1 for male, 2 for female, the Linear Regression would have treated them as weights. How do I replace the Gender column with [1,0] or [0,1], which I learn is the right way to represent categorical data? Would it be a dictionary type column or a list type column? If so how will it be part of the Numpy array?
This is called indicator or dummy variables, and Pandas allows to easily encode such categorical values:
>>> import pandas as pd
>>> pd.get_dummies(['male', 'female'])
female male
0 0 1
1 1 0
Don't forget about multicollinearity, though - algorithms like linear regression rely on independence of variables, while in your case female=0 definitely means male=1. In this case simply remove one dummy variable (e.g. use only female var and not male).
There is also a LabelEncoder() in sklearn.preprocessing package:
from sklearn import preprocessing
le1 = preprocessing.LabelEncoder()
y = le1.transform(y)
You can also inverse transform back with le1.inverse_transform(y).
The encoding is done automatically though, you cannot change the order.