Sklearn can't convert string to float

Sklearn can't convert string to float - python

I'm using Sklearn as a machine learning tool, but every time I run my code, it gives this error:
Traceback (most recent call last):
File "C:\Users\FakeUserMadeUp\Desktop\Python\Machine Learning\MachineLearning.py", line 12, in <module>
model.fit(X_train, Y_train)
File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\sklearn\tree\_classes.py", line 942, in fit
X_idx_sorted=X_idx_sorted,
File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\sklearn\tree\_classes.py", line 166, in fit
X, y, validate_separately=(check_X_params, check_y_params)
File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\sklearn\base.py", line 578, in _validate_data
X = check_array(X, **check_X_params)
File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\sklearn\utils\validation.py", line 746, in check_array
array = np.asarray(array, order=order, dtype=dtype)
File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\pandas\core\generic.py", line 1993, in __ array __
return np.asarray(self._values, dtype=dtype)
ValueError: could not convert string to float: 'Paris'
Here is the code, and down below there's my dataset:
(I've tried multiple different datasets, also, this dataset is a txt because I made it myself and am to dumb to convert it to csv.)
import pandas as pd
from sklearn.tree import DecisionTreeClassifier as dtc
from sklearn.model_selection import train_test_split as tts
city_data = pd.read_csv('TimeZoneTable.txt')
X = city_data.drop(columns=['Country'])
Y = city_data['Country']
X_train, X_test, Y_train, Y_test = tts(X, Y, test_size = 0.2)
model = dtc()
model.fit(X_train, Y_train)
predictions = model.predict(X_test)
print(Y_test)
print(predictions)
Dataset:
CityName,Country,Latitude,Longitude,TimeZone
Moscow,Russia,55.45'N,37.37'E,3
Vienna,Austria,48.13'N,16.22'E,2
Barcelona,Spain,41.23'N,2.11'E,2
Madrid,Spain,40.25'N,3.42'W,2
Lisbon,Portugal,38.44'N,9.09'W,1
London,UK,51.30'N,0.08'W,1
Cardiff,UK,51.29'N,3.11'W,1
Edinburgh,UK,55.57'N,3.11'W,1
Dublin,Ireland,53.21'N,6.16'W,1
Paris,France,48.51'N,2.21'E,2

Machine learning algorithms and in particular the random forest work exclusively with input numbers. If you want to improve your model it is even recommended to normalize your model between -1;1 in general and therefore to use decimal numbers, hence the expectation of a float.
In your case, your dataframe seems to contain exclusively string entries. As Dilara Gokay said, you first need to transform your strings into floats and to do so, use what is called an onehotencoder. I let you follow this tutorial if you don't know how to do it.

Related

how to use CTR as dependent variable for a logistic regression (Python)

I want to use a logistic regression to forecast the click Through rate (CTR) of advertisements. As independent variables i use variables like placement and different binary variables on the creative. As dependent variable i want to use the CTR but i don't know how to do this because i need a binary variable as dependent variable for a logistic regression. A small example of the variables is have for the dependent variables are shown below.
My question is how can i use these variables to construct a logistic regression? Preferably in Python.
Clicks : 1148, 9952, 1329
Impressions : 251091, 1051109, 208390
CTR : 0.457, 0.947, 0.638
Thank you in advance
The code i used was:
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, test_size=0.20, random_state=0)
logisticRegr = LogisticRegression()
logisticRegr.fit(x_train, y_train)
predictions = logisticRegr.predict(x_test) # x here is a matrix of independent mostly binary variables. Y = CTR which is a variable ranging between 0 and 1
The error i got was:
Traceback (most recent call last): File site-packages\IPython\core\interactiveshell.py", line 3442, in run_code exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-16-d7379bff0c70>", line 2, in <module> logisticRegr.fit(x_train, y_train) File "site-packages\sklearn\linear_model_logistic.py",
line 1204, in fit check_classification_targets(y) File site-packages\sklearn\utils\multiclass.py",
line 207, in check_classification_targets raise ValueError("Unknown label type: %r" % y_type)
ValueError: Unknown label type: 'continuous'

The problem comes from the very nature of CTR variable which is continuous where it must be binary (0 or 1). Changing your data in your MCVE, the model trains et predicts as expected:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
data = {
"clicks": [1148, 9952, 1329],
"impressions": [251091, 1051109, 208390],
#"CTR": [0.457, 0.947, 0.638],
"CTR": [0, 1, 1]
}
X = pd.DataFrame(data)
y = X.pop("CTR")
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)
model = LogisticRegression()
model.fit(x_train.values, y_train.values)
y_hat = model.predict(x_test)

How to predict data from scikit-learn toy dataset

I am studying machine learning and I am trying to analyze the scikit diabetes toy database. In this case, I want to change the default Bunch object to a pandas DataFrame object. I tried using the argument as_frame=True and it did actually change the object type to DataFrame.
So after that, I trained the data and the problems come when I'm trying to plot it:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split
dataset = datasets.load_diabetes(as_frame=True)
X = dataset.data
y = dataset.target
y = y.to_frame()
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=42)
regressor = linear_model.LinearRegression()
regressor.fit(X_train, y_train)
plt.scatter(X_train, y_train, color='blue')
plt.plot(X_train, regressor.predict(X_test), color='red')
The problem is when I am trying to plot it using matplotlib, since the as_frame=True returns (data, target) where the data is a DataFrame object and target as Series.
Traceback (most recent call last):
File "C:/Users/Kelvin/OneDrive/Documents/analytics/diabetes-sklearn/test.py", line 19, in <module>
plt.scatter(X_train, y_train, color='blue')
File "C:\Users\Kelvin\OneDrive\Desktop\analytics\lib\site-packages\matplotlib\pyplot.py", line 3037, in scatter
__ret = gca().scatter(
File "C:\Users\Kelvin\OneDrive\Desktop\analytics\lib\site-packages\matplotlib\__init__.py", line 1352, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "C:\Users\Kelvin\OneDrive\Desktop\analytics\lib\site-packages\matplotlib\axes\_axes.py", line 4478, in scatter
raise ValueError("x and y must be the same size")
ValueError: x and y must be the same size
So, my question is if there are ways that I can change the whole data as DataFrame just like how we get the data using pd.read_csv()?

That is already a dataframe, you are getting error because you are plotting X_train with y_train and X_train has multiple columns.
but if you want your dataset in csv file you can use this code.
X.to_csv('train_data.csv')
this will save that dataset into a csv file in your working directory. Now you can use pd.read_csv on train_data.csv.

AttributeError: 'numpy.ndarray' object has no attribute 'lower'

I am trying to predict using SVM but I receive the error
AttributeError: 'numpy.ndarray' object has no attribute 'lower'
when executing line text_clf.fit(X_train,y_train) of my code. How to fix this and get the probability that my prediction is correct using SVM?
I am predicting the first column (gold) of my input file based on the values of the remaining columns. My input file dataExtended.txtis under the form:
gold,T-x-T,T-x-N,T-x-U,T-x-NT,T-x-UT,T-x-UN,T-x-UNT,N-x-T,N-x-N,N-x-U,N-x-NT,N-x-UT,N-x-UN,N-x-UNT,U-x-T,U-x-N,U-x-U,U-x-NT,U-x-UT,U-x-UN,U-x-UNT,NT-x-T,NT-x-N,NT-x-U,NT-x-NT,NT-x-UT,NT-x-UN,NT-x-UNT,UT-x-T,UT-x-N,UT-x-U,UT-x-NT,UT-x-UT,UT-x-UN,UT-x-UNT,UN-x-T,UN-x-N,UN-x-U,UN-x-NT,UN-x-UT,UN-x-UN,UN-x-UNT,UNT-x-T,UNT-x-N,UNT-x-U,UNT-x-NT,UNT-x-UT,UNT-x-UN,UNT-x-UNT,T-T-x,T-N-x,T-U-x,T-NT-x,T-UT-x,T-UN-x,T-UNT-x,N-T-x,N-N-x,N-U-x,N-NT-x,N-UT-x,N-UN-x,N-UNT-x,U-T-x,U-N-x,U-U-x,U-NT-x,U-UT-x,U-UN-x,U-UNT-x,NT-T-x,NT-N-x,NT-U-x,NT-NT-x,NT-UT-x,NT-UN-x,NT-UNT-x,UT-T-x,UT-N-x,UT-U-x,UT-NT-x,UT-UT-x,UT-UN-x,UT-UNT-x,UN-T-x,UN-N-x,UN-U-x,UN-NT-x,UN-UT-x,UN-UN-x,UN-UNT-x,UNT-T-x,UNT-N-x,UNT-U-x,UNT-NT-x,UNT-UT-x,UNT-UN-x,UNT-UNT-x,x-T-T,x-T-N,x-T-U,x-T-NT,x-T-UT,x-T-UN,x-T-UNT,x-N-T,x-N-N,x-N-U,x-N-NT,x-N-UT,x-N-UN,x-N-UNT,x-U-T,x-U-N,x-U-U,x-U-NT,x-U-UT,x-U-UN,x-U-UNT,x-NT-T,x-NT-N,x-NT-U,x-NT-NT,x-NT-UT,x-NT-UN,x-NT-UNT,x-UT-T,x-UT-N,x-UT-U,x-UT-NT,x-UT-UT,x-UT-UN,x-UT-UNT,x-UN-T,x-UN-N,x-UN-U,x-UN-NT,x-UN-UT,x-UN-UN,x-UN-UNT,x-UNT-T,x-UNT-N,x-UNT-U,x-UNT-NT,x-UNT-UT,x-UNT-UN,x-UNT-UNT,callersAtLeast1T,CalleesAtLeast1T,callersAllT,calleesAllT,CallersAtLeast1N,CalleesAtLeast1N,CallersAllN,CalleesAllN,childrenAtLeast1T,parentsAtLeast1T,childrenAtLeast1N,parentsAtLeast1N,childrenAllT,parentsAllT,childrenAllN,ParentsAllN,ParametersatLeast1T,FieldMethodsAtLeast1T,ReturnTypeAtLeast1T,ParametersAtLeast1N,FieldMethodsAtLeast1N,ReturnTypeN,ParametersAllT,FieldMethodsAllT,ParametersAllN,FieldMethodsAllN,ClassGoldN,ClassGoldT,Inner,Leaf,Root,Isolated,EmptyCallers,EmptyCallees,EmptyCallersCallers,EmptyCalleesCallees,Program,Requirement,MethodID
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N,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,chess,4,3
N,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,chess,5,3
Here is my full reproducible code:
# Make Predictions with Naive Bayes On The Iris Dataset
from sklearn.cross_validation import train_test_split
from sklearn import metrics
import pandas as pd
import numpy as np
import seaborn as sns; sns.set()
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
import seaborn as sns
from sklearn import svm
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
data = pd.read_csv( 'dataExtended.txt', sep= ',')
row_count, column_count = data.shape
# Printing the dataswet shape
print ("Dataset Length: ", len(data))
print ("Dataset Shape: ", data.shape)
print("Number of columns ", column_count)
# Printing the dataset obseravtions
print ("Dataset: ",data.head())
data['gold'] = data['gold'].astype('category').cat.codes
data['Program'] = data['Program'].astype('category').cat.codes
# Building Phase Separating the target variable
X = data.values[:, 1:column_count]
Y = data.values[:, 0]
# Splitting the dataset into train and test
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size = 0.3, random_state = 100)
#Create a svm Classifier
svclassifier = svm.LinearSVC()
print('Before fitting')
svclassifier.fit(X_train, y_train)
predicted = svclassifier.predict(X_test)
text_clf = Pipeline([('tfidf',TfidfVectorizer()),('clf',LinearSVC())])
text_clf.fit(X_train,y_train)
Traceback leading to error:
Traceback (most recent call last):
File "<ipython-input-9-8e85a0a9f81c>", line 1, in <module>
runfile('C:/Users/mouna/ownCloud/Mouna Hammoudi/dumps/Python/Paper4SVM.py', wdir='C:/Users/mouna/ownCloud/Mouna Hammoudi/dumps/Python')
File "C:\Users\mouna\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile
execfile(filename, namespace)
File "C:\Users\mouna\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/mouna/ownCloud/Mouna Hammoudi/dumps/Python/Paper4SVM.py", line 53, in <module>
text_clf.fit(X_train,y_train)
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 248, in fit
Xt, fit_params = self._fit(X, y, **fit_params)
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 213, in _fit
**fit_params_steps[name])
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py", line 362, in __call__
return self.func(*args, **kwargs)
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 581, in _fit_transform_one
res = transformer.fit_transform(X, y, **fit_params)
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1381, in fit_transform
X = super(TfidfVectorizer, self).fit_transform(raw_documents)
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 869, in fit_transform
self.fixed_vocabulary_)
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 792, in _count_vocab
for feature in analyze(doc):
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 266, in <lambda>
tokenize(preprocess(self.decode(doc))), stop_words)
File "C:\Users\mouna\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 232, in <lambda>
return lambda x: strip_accents(x.lower())

You cannot use TF-IDF-related methods for numeric data; the method is exclusively for use with text data, hence it uses methods such as .tolower(), which are by default applicable to strings, hence the error. This is already apparent from the documentation:
fit(self, raw_documents, y=None)
Learn vocabulary and idf from training set.
Parameters
raw_documents: iterable
An iterable which yields either str, unicode or file objects.
I am afraid that your rationale, as explained in the comments:
I'm just trying to get the probability that each prediction is correct and TF-IDF seems to be the only way to do so when using SVM
is extremely weak. For starters, there is no such thing as "the probability that each prediction is correct" - I take it that you mean probabilistic predictions, in contrast to hard class predictions (see Predict classes or class probabilities?)
To get to the point of your actual requirement: in contrast to LinearSVC, which you are using here, SVC does indeed provide a predict_proba method, which should do the job (see the docs and the instructions therein). Notice that LinearSVC is not actually an SVM - see answer in Under what parameters are SVC and LinearSVC in scikit-learn equivalent? for details.
In short, forget about TF-IDF and switch to SVC instead of LinearSVC.

GradientBoostingClassifier implementation

I want to implement Gradient Boosting Classifier to my Titanic ML solution based on sklearn library.
I use VS Code on Ubuntu 18.04.
I've tried:
# Splitting the Training Data
from sklearn.model_selection import train_test_split
predictors = train.drop(['Survived', 'PassengerId'], axis=1)
target = train["Survived"]
x_train, x_val, y_train, y_val = train_test_split(predictors,
target, test_size = 0.22, random_state = 0)
# Gradient Boosting Classifier
from sklearn.ensemble import GradientBoostingClassifier
gbk = GradientBoostingClassifier()
gbk.fit(x_train, y_train)
..which returns:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py", line 1395, in fit
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 756, in check_X_y
estimator=estimator)
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 527, in check_array
array = np.asarray(array, dtype=dtype, order=order)
File "/home/sj/anaconda3/lib/python3.7/site-packages/numpy/core/numeric.py", line 501, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: 'Baby'
Help would be appreciated. I'm quite new to DS.

I think you may a non numerical values in your train data. Your classifier can take numerical inputs. That's why it tries to convert a string, here 'Baby', to a float. As this operation is not supported, it fails.
Maybe look again at your data.

ValueError: "metrics can't handle a mix of binary and continuous targets" with no source

I'm a beginner in Machine Learning and I'm trying to learn by working through Kaggle's Titanic problem. From what I know, I've made sure that the metrics are in sync with one another but of course I blame myself for this problem and not Python. However, I still couldn't find the source and Spyder IDE is no help.
This is my code:
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
"""Assigning the train & test datasets' adresses to variables"""
train_path = "C:\\Users\\Omar\\Downloads\\Titanic Data\\train.csv"
test_path = "C:\\Users\\Omar\\Downloads\\Titanic Data\\test.csv"
"""Using pandas' read_csv() function to read the datasets
and then assigning them to their own variables"""
train_data = pd.read_csv(train_path)
test_data = pd.read_csv(test_path)
"""Using pandas' factorize() function to represent genders (male/female)
with binary values (0/1)"""
train_data['Sex'] = pd.factorize(train_data.Sex)[0]
test_data['Sex'] = pd.factorize(test_data.Sex)[0]
"""Replacing missing values in the training and test dataset with 0"""
train_data.fillna(0.0, inplace = True)
test_data.fillna(0.0, inplace = True)
"""Selecting features for training"""
columns_of_interest = ['Pclass', 'Sex', 'Age']
"""Dropping missing/NaN values from the training dataset"""
filtered_titanic_data = train_data.dropna(axis=0)
"""Using the predictory features in the data to handle the x axis"""
x = filtered_titanic_data[columns_of_interest]
"""The survival (what we're trying to find) is the y axis"""
y = filtered_titanic_data.Survived
"""Splitting the train data with test"""
train_x, val_x, train_y, val_y = train_test_split(x, y, random_state=0)
"""Assigning the DecisionTreeRegressor model to a variable"""
titanic_model = DecisionTreeRegressor()
"""Fitting the x and y values with the model"""
titanic_model.fit(train_x, train_y)
"""Predicting the x-axis"""
val_predictions = titanic_model.predict(val_x)
"""Assigning the feature columns from the test to a variable"""
test_x = test_data[columns_of_interest]
"""Predicting the test by feeding its x axis into the model"""
test_predictions = titanic_model.predict(test_x)
"""Printing the prediction"""
print(val_predictions)
"""Checking for the accuracy"""
print(accuracy_score(val_y, val_predictions))
"""Printing the test prediction"""
print(test_predictions)
and this is the stacktrace:
Traceback (most recent call last):
File "<ipython-input-3-73797c87986e>", line 1, in <module>
runfile('C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py', wdir='C:/Users/Omar/Downloads/Kaggle Competition')
File "C:\Users\Omar\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
execfile(filename, namespace)
File "C:\Users\Omar\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py", line 58, in <module>
print(accuracy_score(val_y, val_predictions))
File "C:\Users\Omar\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 176, in accuracy_score
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "C:\Users\Omar\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 81, in _check_targets
"and {1} targets".format(type_true, type_pred))
ValueError: Classification metrics can't handle a mix of binary and continuous targets

You are trying to use a regression algorithm (DecisionTreeRegressor) for a binary classification problem; the regression model, as expected, gives continuous outputs, but the accuracy_score, where the error actually happens:
File "C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py", line 58, in <module>
print(accuracy_score(val_y, val_predictions))
expects binary ones, hence the error.
For starters, change your model to
from sklearn.tree import DecisionTreeClassifier
titanic_model = DecisionTreeClassifier()

You are using a DecisionTreeRegressor, which as it says, is a regressor model. The Kaggle Titanic problem is a classification problem. So you should use a DecisionTreeClassifier.
As for why your code is throwing an error, it is because val_y has binary values (0,1) whereas val_predictions has continuous values because you used a Regressor model.

Classification needs discrete label as it predicts class(which is any one of the label) and Regression works with continuous data. As your output is class label, you need to perform classification

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Sklearn can't convert string to float - python

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ValueError: "metrics can't handle a mix of binary and continuous targets" with no source

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