Was trying to run the same code as per the SCIKIT user guide of Grid search but giving error.Quite surprised.
from sklearn.model_selection import GridSearchCV
from sklearn.calibration import CalibratedClassifierCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
X,y=make_moons()
calibrated_forest=CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators=10))
paramgrid={'base_estimator_max_depth':[2,4,6,8]}
search=GridSearchCV(calibrated_forest,paramgrid,cv=5)
search.fit(X,y)
Error message as below:
ValueError: Invalid parameter base_estimator_max_depth for estimator CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators=10)). Check the list of available parameters with `estimator.get_params().keys()`.
I tried with Iris data set which also gave the same error as above.
Then i used the make_moon dataset X,y and run the Random classifier as below.
clf = RandomForestClassifier(n_estimators=10, max_depth=2)
cross_val_score(clf, X, y, cv=5)
Got the output as below.
array([0.8 , 0.8 , 0.9 , 0.95, 0.95])
Looking strange and not sure what is happening and where iam wrong. Request help please.
Note the double score __ between base_estimator and a param:
from sklearn.model_selection import GridSearchCV
from sklearn.calibration import CalibratedClassifierCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
X,y=make_moons()
calibrated_forest=CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators=10))
paramgrid={'base_estimator__max_depth':[2,4,6,8]}
search=GridSearchCV(calibrated_forest,paramgrid,cv=5)
search.fit(X,y)
GridSearchCV(cv=5,
estimator=CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators=10)),
param_grid={'base_estimator__max_depth': [2, 4, 6, 8]})
Related
I'm trying to log the plot of a confusion matrix generated with scikit-learn for a test set using mlflow's support for scikit-learn.
For this, I tried something that resemble the code below (I'm using mlflow hosted on Databricks, and sklearn==1.0.1)
import sklearn.datasets
import pandas as pd
import numpy as np
import mlflow
from sklearn.pipeline import Pipeline
from sklearn.linear_model import SGDClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
mlflow.set_tracking_uri("databricks")
mlflow.set_experiment("/Users/name.surname/plotcm")
data = sklearn.datasets.fetch_20newsgroups(categories=['alt.atheism', 'sci.space'])
df = pd.DataFrame(data = np.c_[data['data'], data['target']])\
.rename({0:'text', 1:'class'}, axis = 'columns')
train, test = train_test_split(df)
my_pipeline = Pipeline([
('vectorizer', TfidfVectorizer()),
('classifier', SGDClassifier(loss='modified_huber')),
])
mlflow.sklearn.autolog()
from sklearn.metrics import ConfusionMatrixDisplay # should I import this after the call to `.autolog()`?
my_pipeline.fit(train['text'].values, train['class'].values)
cm = ConfusionMatrixDisplay.from_predictions(
y_true=test["class"], y_pred=my_pipeline.predict(test["text"])
)
while the confusion matrix for the training set is saved in my mlflow run, no png file is created in the mlflow frontend for the test set.
If I try to add
cm.figure_.savefig('test_confusion_matrix.png')
mlflow.log_artifact('test_confusion_matrix.png')
that does the job, but requires explicitly logging the artifact.
Is there an idiomatic/proper way to autolog the confusion matrix computed using a test set after my_pipeline.fit()?
The proper way to do this is to use mlflow.log_figure as a fluent API announced in MLflow 1.13.0. You can read the documentation here. This code will do the job.
mlflow.log_figure(cm.figure_, 'test_confusion_matrix.png')
This function implicitly store the image, and then calls log_artifact against that path, something like you did.
How to fix this
from sklearn.datasets import load_iris
import os
import math
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import csv
import pandas as pd
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import seaborn as sns
iris=load_iris()
print(iris.keys())
print(iris['target'].shape)
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(iris['data'],iris['target'],random_state=0)
print(y_train.shape)
print(X_train.shape)
iris_dataframe=pd.DataFrame(X_train,columns=iris.feature_names)
knn=KNeighborsClassifier(n_neighbors=1)
y_pred = knn.predict(X_train) //error:NotFittedError: This KNeighborsClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.
what is the error?
please help me!!!! I don't know how to fix this
You'll want to start by training you K-means model (computing clusters on training data): knn.fit(X_train, y_train).
You can then use it to classify your testing data, using the cluster centers computed during the training phase: y_pred = knn.predict(X_test).
I wanna simultaneously apply cross-validation and over-sampling.
I get the following error from this code:
from sklearn.pipeline import Pipeline, make_pipeline
imba_pipeline = make_pipeline(SMOTE(random_state=42),
LogisticRegression(C=3.4))
cross_val_score(imba_pipeline, X_train_tf, y_train, scoring='f1-weighted', cv=kf)
ll intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'SMOTE(k_neighbors=5, kind='deprecated', m_neighbors='deprecated', n_jobs=1,
out_step='deprecated', random_state=42, ratio=None,
sampling_strategy='auto', svm_estimator='deprecated')' (type ) doesn't
PS. I get the same error using imblearn.over_sampling.RandomOverSampler rather than SMOTE.
You should import make_pipeline from imblearn.pipeline and not from sklearn.pipeline: make_pipeline from sklearn needs the transformers to implement fit and transform methods but SMOTE does not implement transform.
I try to test a first example using sklearn:
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
X = [[0.44, 0.68], [0.99, 0.23]]
vector = [109.85, 155.72]
predict= [0.49, 0.18]
poly = PolynomialFeatures(degree=2)
X_ = poly.fit_transform(X)
predict_ = poly.fit_transform(predict)
clf = linear_model.LinearRegression()
clf.fit(X_, vector)
print clf.predict(predict_)
But i have these errors:
/usr/lib/python2.7/dist-packages/scipy/sparse/csgraph/__init__.py:148:
RuntimeWarning: numpy.dtype size changed, may indicate binary
incompatibility
from ._shortest_path import shortest_path, floyd_warshall, dijkstra,\
/usr/lib/python2.7/dist-packages/scipy/sparse/csgraph/_validation.py:5:
RuntimeWarning: numpy.dtype size changed, may indicate binary
incompatibility
File "hi.py", line 1, in <module>
from sklearn.preprocessing import PolynomialFeatures
ImportError: cannot import name PolynomialFeatures
python -V --> 2.7.6
Please, how can I deal with these errors?
Bests.
You can check your sklearn version, use:
import sklearn
print('Version {}.'.format(sklearn.__version__))
For me it shows:
Version 0.17.1.
Then check (from help of PolynomialFeatures) which version offers PolynomialFeatures and make an update. If your version is 0.14.1 or below, you will get this error. Check this page for more details on how to upgrade it: Not able to import PolynomialFeatures, make_pipeline in Scikit-learn (Official: http://scikit-learn.org/stable/install.html)
Today I've tried to test an amazing Catboost library published recently by Yandex but it shows very poor results even on a toy dataset. I've tried to find a root of my problem but due to the lack of proper documentation and topics about the library I can't figure out what's going on. Please help me =)
I'm using Anaconda 3 x64 with Python 3.6.
from sklearn.datasets import make_classification
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, roc_curve, f1_score, make_scorer
from catboost import CatBoostClassifier
X,y = make_classification( n_classes=2
,n_clusters_per_class=2
,n_features=10
,n_informative=4
,n_repeated=2
,shuffle=True
,random_state=564
,n_samples=10000
)
X_train,X_test,y_train,y_test = train_test_split(X,y,train_size = 0.8)
cb = CatBoostClassifier(depth=3,custom_loss=
['Accuracy','AUC'],
logging_level='Silent',
iterations=500,
od_type='Iter',
od_wait=20)
cb.fit(X_train,y_train,eval_set=(X_test,y_test),plot=True,use_best_model=True)
pred = cb.predict_proba(X_test)[:,1]
tpr,fpr,_=roc_curve(y_score=pred,y_true=y_test)
#just to show the difference
from sklearn.ensemble import GradientBoostingClassifier
gbc = GradientBoostingClassifier().fit(X_train,y_train)
pred_gbc = gbc.predict_proba(X_test)[:,1]
tpr_xgb,fpr_xgb,_=roc_curve(y_score=pred_gbc,y_true=y_test)
plt.plot(tpr,fpr,color='orange')
plt.plot(tpr_xgb,fpr_xgb,color='red')
plt.show()
It was a bug. Be careful and ensure you are using the latest version. The bug was fixed in 0.6.1 version.