Develop Reference

How to slice a XGBClassifier/XGBRegressor model into sub-models?

This document shows that a XGBoost API trained model can be sliced by following code:
from sklearn.datasets import make_classification
import xgboost as xgb
booster = xgb.train({
'num_parallel_tree': 4, 'subsample': 0.5, 'num_class': 3},
num_boost_round=num_boost_round, dtrain=dtrain)
sliced: xgb.Booster = booster[3:7]
I tried it and it worked.
Since XGBoost provides Scikit-Learn Wrapper interface, I tried something like this:
from xgboost import XGBClassifier
clf_xgb = XGBClassifier().fit(X_train, y_train)
clf_xgb_sliced: clf_xgb.Booster = booster[3:7]
But got following error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-18-84155815d877> in <module>
----> 1 clf_xgb_sliced: clf_xgb.Booster = booster[3:7]
AttributeError: 'XGBClassifier' object has no attribute 'Booster'
Since XGBClassifier has no attribute 'Booster', is there any way to slice a Scikit-Learn Wrapper interface trained XGBClassifier(/XGBRegressor) model?

The problem is with the type hint you are giving clf_xgb.Booster which does not match an existing argument. Try:
clf_xgb_sliced: xgb.Booster = clf_xgb.get_booster()[3:7]
instead.

AttributeError: LinearRegression object has no attribute 'model'

I saw multiple simmilar questions about the following error in the sklearn:
'AttributeError: LinearRegression object has no attribute...'
I couldn't find any hint about my problem tough:
AttributeError: LinearRegression object has no attribute 'model'
I tried to do a multilinear regression y ~ x with the following code:
import statsmodels.api as sma
from sklearn import linear_model
#https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
#perform linear regression
df_x = df.drop('Migration distance',1) #for simplicity I did't use any testing data just this 2
df_y = df['Migration distance']
reg = linear_model.LinearRegression().fit(df_x, df_y)
reg_score=reg.score(df_x, df_y)
print('R2 score:',reg_score)
#plot the residuals
fig = plt.figure(figsize=(12,8))
fig = sma.graphics.plot_regress_exog(reg, 'Migration distance', fig=fig)
but this error occurs every time I try to plot the residuals:
AttributeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_12904/573591118.py in <module>
10
11 fig = plt.figure(figsize=(12,8))
---> 12 fig = sma.graphics.plot_regress_exog(reg, 'Migration distance', fig=fig)
C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\graphics\regressionplots.py in plot_regress_exog(results, exog_idx, fig)
218 fig = utils.create_mpl_fig(fig)
219
--> 220 exog_name, exog_idx = utils.maybe_name_or_idx(exog_idx, results.model)
221 results = maybe_unwrap_results(results)
222
AttributeError: 'LinearRegression' object has no attribute 'model'
I think my linear regression works because I can compute the R2 score but I have no clue how to overcome this error in order to plot the residuals.

As the documentation for graphics.plot_regress_exog implies, the model passed in the results argument (i.e. your reg here) must be
A result instance with resid, model.endog and model.exog as attributes.
i.e. a statsmodels model, and not a scikit-learn one, as your LinearRegression here. In other words, the function cannot work with scikit-learn models.
Since you are actually doing a simple OLS regression, if you really need the functionality, I would suggest using the respective statsmodels model instead of the scikit-learn one.

RANSAC algorithm using scikit-learn's RANSACRegressor

I tried to use the code below for fitting a robust regression model using RANSAC
from sklearn.linear_model import RANSACRegressor
ransac = RANSACRegressor(LinearRegression(),
max_trials=100,
min_samples=50,
residual_metric=lambda x: np.sum(np.abs(x), axis=1),
residual_threshold=5.0,
random_state=0)
ransac.fit(X,y)
And I get the following error below:
TypeError Traceback (most recent call last)
<ipython-input-38-832d8b5d351b> in <module>
5 residual_metric=lambda x: np.sum(np.abs(x), axis=1),
6 residual_threshold=5.0,
----> 7 random_state=0)
8 ransac.fit(X,y)
TypeError: __init__() got an unexpected keyword argument 'residual_metric'
Can you help me know what's wrong?

Most likely you got this code that was using an old version of ransac. The input residual_metric is deprecated. If you run without that, it works ok:
from sklearn.linear_model import RANSACRegressor, LinearRegression
ransac = RANSACRegressor(LinearRegression(),
max_trials=100,
min_samples=50,
residual_threshold=5.0,
random_state=0)
ransac
RANSACRegressor(base_estimator=LinearRegression(), min_samples=50,
random_state=0, residual_threshold=5.0)

PySpark logistic regression missing method

I am new to Pyspark. I am using logistic regression API. I followed some tutorials and worked this way :
from pyspark.ml.classification import LogisticRegression
train, test = df.randomSplit([0.80, 0.20], seed = some_seed)
LR = LogisticRegression(featuresCol = 'features', labelCol = 'label', maxIter=some_iter)
LR_model = LR.fit(train)
When I call
trainingSummary = LR_model.summary
trainingSummary.roc
I get
--------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-319-bf79768ab64e> in <module>()
1 trainingSummary = LR_model.summary
2
----> 3 trainingSummary.roc
AttributeError: 'LogisticRegressionTrainingSummary' object has no attribute 'roc'
Someone has an idea ?

AttributeError: LinearRegression object has no attribute 'coef_'

I've been attempting to fit this data by a Linear Regression, following a tutorial on bigdataexaminer. Everything was working fine up until this point. I imported LinearRegression from sklearn, and printed the number of coefficients just fine. This was the code before I attempted to grab the coefficients from the console.
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import sklearn
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
boston = load_boston()
bos = pd.DataFrame(boston.data)
bos.columns = boston.feature_names
bos['PRICE'] = boston.target
X = bos.drop('PRICE', axis = 1)
lm = LinearRegression()
After I had all this set up I ran the following command, and it returned the proper output:
In [68]: print('Number of coefficients:', len(lm.coef_)
Number of coefficients: 13
However, now if I ever try to print this same line again, or use 'lm.coef_', it tells me coef_ isn't an attribute of LinearRegression, right after I JUST used it successfully, and I didn't touch any of the code before I tried it again.
In [70]: print('Number of coefficients:', len(lm.coef_))
Traceback (most recent call last):
File "<ipython-input-70-5ad192630df3>", line 1, in <module>
print('Number of coefficients:', len(lm.coef_))
AttributeError: 'LinearRegression' object has no attribute 'coef_'

The coef_ attribute is created when the fit() method is called. Before that, it will be undefined:
>>> import numpy as np
>>> import pandas as pd
>>> from sklearn.datasets import load_boston
>>> from sklearn.linear_model import LinearRegression
>>> boston = load_boston()
>>> lm = LinearRegression()
>>> lm.coef_
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-22-975676802622> in <module>()
7
8 lm = LinearRegression()
----> 9 lm.coef_
AttributeError: 'LinearRegression' object has no attribute 'coef_'
If we call fit(), the coefficients will be defined:
>>> lm.fit(boston.data, boston.target)
>>> lm.coef_
array([ -1.07170557e-01, 4.63952195e-02, 2.08602395e-02,
2.68856140e+00, -1.77957587e+01, 3.80475246e+00,
7.51061703e-04, -1.47575880e+00, 3.05655038e-01,
-1.23293463e-02, -9.53463555e-01, 9.39251272e-03,
-5.25466633e-01])
My guess is that somehow you forgot to call fit() when you ran the problematic line.

I also got the same problem while dealing with linear regression the problem object has no attribute 'coef'.
There are just slight changes in the syntax only.
linreg = LinearRegression()
linreg.fit(X,y) # fit the linesr model to the data
print(linreg.intercept_)
print(linreg.coef_)
I Hope this will help you Thanks