Develop Reference

Sklearn Naive Bayes GaussianNB from .csv

I'm having a problem with sklearn.
When I train it with ".fit()" it shows me the ValueError "ValueError: could not convert string to float: 'Casado'"
This is my code:
"""
from sklearn.naive_bayes import GaussianNB
import pandas as pd
# 1. Create Naive Bayes classifier:
gaunb = GaussianNB()
# 2. Create dataset:
dataset = pd.read_csv("archivos_de_datos/Datos_Historicos_Clientes.csv")
X_train = dataset.drop(["Compra"], axis=1) #Here I removed the last column "Compra"
Y_train = dataset["Compra"] #This one only consists of that column "Compra"
print("X_train: ","\n", X_train)
print("Y_train: ","\n", Y_train)
dataset2 = pd.read_csv("archivos_de_datos/Nuevos_Clientes.csv")
X_test = dataset2.drop("Compra", axis=1)
print("X_test: ","\n", X_test)
# 3. Train classifier with dataset:
gaunb = gaunb.fit(X_train, Y_train) #Here shows "ValueError: could not convert string to float: 'Casado'"
# 4. Predict using classifier:
prediction = gaunb.predict(X_test)
print("PREDICTION: ",prediction)
"""
And the dataset I'm using is an .csv file that looks like this (but with more rows):
IdCliente,EstadoCivil,Profesion,Universitario,TieneVehiculo,Compra
1,Casado,Empresario,Si,No,No
2,Casado,Empresario,Si,Si,No
3,Soltero,Empresario,Si,No,Si
I'm trying to train it to determine (with a test dataset) whether the last column would be a Yes or No (Si or No)
I appreciate your help, I'm obviously new at this and I don't understand what am I doing wrong here

I would use onehotencoder to, like Lavin mentioned, make the yes or no a numerical value. A model such as this can't process categorical data.
Onehotencoder is used to handle binary data such as yes/no, male/female, while label encoder is used for categorical data with more than 2 values, ei, country names.
It will look something like this, however, you'll have to do this with all categorical data, not just your y column, and use label encoder for columns that are not binary ( more than 2 variables - for example, perhaps Estadio Civil)
Also I would suggest removing any dependent variables that don't contribute to your model, for instant client ID sounds like it may not add any value in determining your dependent variable. This is context specific, but something to keep in mind.
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [Insert column number for your df])], remainder='passthrough')
X = np.array(ct.fit_transform(X))
For the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html
More info:
https://contactsunny.medium.com/label-encoder-vs-one-hot-encoder-in-machine-learning-3fc273365621#:~:text=What%20one%20hot%20encoding%20does,which%20column%20has%20what%20value.&text=So%2C%20that's%20the%20difference%20between%20Label%20Encoding%20and%20One%20Hot%20Encoding.

Loading CSV to Scikit Learn

I'm new to python but I'm trying to run a regression with a bunch of different variables. So far I've got it down to Scikit. I've been searching for hours but can't seem to find a way to import the data and then run a linear regression on it while returning the coefficients of each variable. Any help is much appreciated. I have 15 columns that I want to run against the X.
X = Margin
Ys = A1, B1, C1, D1 etc.
Example set below:
Margin,A1
-8,110.7
-10,112
-1,106.7
9,109
-9,107.5
1,108.1
-19,109.2
Here's what I've got so far I know it's not much
import pandas as pd
data = pd.read_csv("NBA.csv")

As a convention in machine learning we consider X as the features and Y as the target.
If you want to run a linear regression and extract the coefficients, you can do the following :
# import the needed libraries
import pandas as pd
from sklearn.linear_model import LinearRegression
# Import the data
data = pd.read_csv("NBA.csv")
# Specify the features and the target
target = 'Margin'
features = data.columns.tolist() # This is the column names of your data as a list
features.remove(target) # We remove the target from the list of features
# Train the model
model = LinearRegression() # Instantiate the model
model.fit(data[features].values, data[target].values) # fit the model to the data
print(features) # Returns the name of each feature
print(model.coef_) # Returns the coefficients for each feature (in the same order of your features)

I would like to consider a feature set(vector) for a data in python for my machine learning algorithm. How can I do it?

I have data in the following form
Class Feature set list
classlabel1 - [size,time] example:[6780.3,350.00]
classlabel2 - [size,time]
classlabel3 - [size,time]
classlabel4 - [size,time]
How do I save this data in excel sheet and how can I train the model using this feature set? Currently I am working on SVM classifier.
I have tried saving the feature set list in a dataframe and saving this dataframe to a csv file. But the size and time are getting split into two different columns.
The dataframe is getting saved in csv file in the following way:
col 0 col1 col2
62309 396.5099154 label1
I would like to train and test on the feature vector [size,time] combined. Is it possible and is this a right way? If it is possible, how can I do it?

Firstly responding to your question:
I would like to train and test on the feature vector [size,time]
combined. Is it possible and is this a right way? If it is possible,
how can I do it?
Combining the two is not the right thing to do because both are in two different scales (if they are actually what there name suggests) and also combining them will result in loss of information which they will provide, so they are two totally independent features for any ML supervised algorithm. So I would suggest to treat these two features separately rather than combining into one.
Now let's move onto to next section:
How do I save this data in excel sheet and how can I train the model
using this feature set? Currently I am working on SVM classifier.
Storing data : In my opinion, you can store data in whichever format you want but I would prefer storing data in csv format as it is convenient and loading of data file is faster.
sample_data.csv
size,time,class_label
100,150,label1
200,250,label2
240,180,label1
Below is the code for reading the data from csv and training SVM :
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# loading data
data = pd.read_csv("sample_data.csv", error_bad_lines=True,
warn_bad_lines=True)
# Dividing into dependent and independent features
Y = data.class_label_col.values
X = data.drop("class_label_col", axis=1).values
# encode the class column values
label_encoded_Y = preprocessing.LabelEncoder().fit_transform(list(Y))
# split training and testing data
x_train,x_test,y_train,y_test=train_test_split(X,label_encoded_Y,
train_size=0.8,
test_size=0.2)
# Now use the whichever trainig algo you want
clf = SVC(gamma='auto')
clf.fit(x_train, y_train)
# Using the predictor
y_pred = clf.predict(x_test)

Since size and time are different features, you should separate them into 2 different columns so your model could set separate weight to each of them, i.e.
# data.csv
size time label
6780.3 3,350.00 classLabel1
...
If you want to transform the data you have into the format above you could use pandas.read_excel and use ast to transform the string list into python list object.
import pandas as pd
import ast
df = pd.read_excel("data.xlsx")
size_time = [(ast.literal_eval(x)[0], ast.literal_eval(x)[1]) for x in df["Feature set list"]]
size = [x[0] for x in size_time]
time = [x[1] for x in size_time]
label = df["Class"]
new_df = pd.DataFrame({"size":size, "time":time, "label":label})
# This will result in the DataFrame below.
# size time label
# 6780.3 350.0 classlabel1
# Save DataFrame to csv
new_df.to_csv("data_fix.csv")
# Use it
x = new_df.drop("label", axis=1)
y = new_df.label
# Further data preparation, such as split the dataset
# into train and test set, etc.
...
Hope this helps

What is leaf_values from Python LightGBM?

I'm using the LightGBM Package.
I have successfully created a new tree using "create_tree_digraph" but I face some trouble understanding the result.
There is "leaf_value" in a leaf node. I don't know what it means. Please, somebody help me understand this. Thanks. :)
I used this example code from here: https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/
#importing standard libraries
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import graphviz
import lightgbm as lgb
#loading our training dataset 'adult.csv' with name 'data' using pandas
data=pd.read_csv('./adult.csv',header=None)
#Assigning names to the columns
data.columns=['age','workclass','fnlwgt','education','education-num','marital_Status','occupation','relationship','race','sex','capital_gain','capital_loss','hours_per_week','native_country','Income']
# Label Encoding our target variable
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
l=LabelEncoder()
l.fit(data.Income)
data.Income=Series(l.transform(data.Income)) #label encoding our target variable
#One Hot Encoding of the Categorical features
one_hot_workclass=pd.get_dummies(data.workclass)
one_hot_education=pd.get_dummies(data.education)
#removing categorical features
data.drop(['workclass','education','marital_Status','occupation','relationship','race','sex','native_country'],axis=1,inplace=True)
#Merging one hot encoded features with our dataset 'data'
data=pd.concat([data,one_hot_workclass,one_hot_education],axis=1)
#Here our target variable is 'Income' with values as 1 or 0.
#Separating our data into features dataset x and our target dataset y
x=data.drop('Income',axis=1)
y=data.Income
#Imputing missing values in our target variable
y.fillna(y.mode()[0],inplace=True)
#Now splitting our dataset into test and train
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.3)
train_data=lgb.Dataset(x_train,label=y_train)
#setting parameters for lightgbm
param = {'num_leaves':150, 'objective':'binary','max_depth':3,'learning_rate':.05,'max_bin':200}
param['metric'] = ['auc', 'binary_logloss']
#training our model using light gbm
num_round=50
lgbm=lgb.train(param,train_data,num_round)
graph = lgb.create_tree_digraph(lgbm)
graph.render(view=True)
Then I applied 'create_tree_digraph' function.
Pics

These are the raw predicted probabilities before the sigmoid function is applied. However, one thing to be aware of is your image is only showing 1 tree out of the entire model so it will not be the same as the actual outcome (unless your model is just this 1 tree).
This Image is showing what it would look like if you applied the sigmoid to the leaf values prior to creating the plots.

ValueError using sklearn and pandas for decision trees?

I'm new to scikit learn and I just saw the documentation and a couple of other stackoverflow posts to build a decision tree.
I have a CSV data set with 16 attributes and 1 target label. How should I pass it into the decision tree classifier?
My current code looks like this:
import pandas
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import tree
data = pandas.read_csv("yelp_atlanta_data_labelled.csv", sep=',')
vect = TfidfVectorizer()
X = vect.fit_transform(data)
Y = data['go']
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
When I run the code it gives me the following error:
ValueError: Number of labels=501 does not match number of samples=17
To give some context, my data set has 501 data points and 17 total columns. The go column is the target column with yes/no labels.

The problem is TfidfVectorizer cannot operate on a dataframe directly. It can only operate on a sequence of strings. Because you are passing a dataframe, it sees it as a sequence of columns and attempts to vectorize each column separately.
Try instead using:
X = vect.fit_transform(data['my_column_name'])
You may want to preprocess the dataframe to concatenate different columns prior to calling vect.fit_transform.