I wanted to find an optimal model to solve the assigned classification problem. Everything went smooth before I applied pd.get_dummies() function to preprocess the data. The experiment showed a impossibly perfect result. I know it is unlikely to happen but I do not know why. Any help would be highly appreciated.
Code for preprocessing data is as below
# Encoding Booking Status
status_dict = {'Not_Canceled':1, 'Canceled':0}
df.booking_status = df.booking_status.map(status_dict)
df.drop('Booking_ID',axis=1, inplace=True)
df = df.dropna()
df = pd.get_dummies(df)
# Standardizing Data
from sklearn.preprocessing import StandardScaler
import numpy as np
X = df.iloc[:,0:-1]
y = df.iloc[:,-1]
scaler = StandardScaler().fit(X)
rescaledX = scaler.transform(X)
np.set_printoptions(precision=3)
print(rescaledX[0:5,:])
And I split my data into training and testing with a proportion of 0.3
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(rescaledX, y, test_size=0.3, random_state=15)
I used several models and the amazing result is
enter image description here
Simple code, stupid me. By the way, just a beginner in ML field. Any advice to master it well?
It was caused by data leaks. You must split your data first before any data pre-processing step. For example,
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(rescaledX, y, test_size=0.3, random_state=15)
Then do your data scaling part on the training and test data separately.
scaler = StandardScaler().fit(X_train)
rescaledX = scaler.transform(X_train)
You could try to use Pipe line as well to avoid data leaks.
# correct data preparation for model evaluation with k-fold cross-validation
from numpy import mean
from numpy import std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
# define dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)
# define the pipeline
steps = list()
steps.append(('scaler', MinMaxScaler()))
steps.append(('model', LogisticRegression()))
pipeline = Pipeline(steps=steps)
# define the evaluation procedure
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate the model using cross-validation
scores = cross_val_score(pipeline, X, y, scoring='accuracy', cv=cv, n_jobs=-1)
# report performance
print('Accuracy: %.3f (%.3f)' % (mean(scores)*100, std(scores)*100))
Ref: https://machinelearningmastery.com/data-preparation-without-data-leakage/
Related
Let's take data
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)
And consider code following :
#Defining X,y - independent variable and dependent variables
X=df.drop(df.columns[[1]], axis=1)
y = (df[1] == 'B').astype(int)
clf=LogisticRegression(solver="lbfgs")
kfold = StratifiedKFold(n_splits=10, shuffle=True)
for train, validation in kfold.split(X, y):
# Fit the model
clf.fit(X[train], y[train])
And the following error occurs :
Do you have any idea why it occurs ? I think I did really not complicated things, so I'm not sure what exactly I did wrong.
X is a DataFrame so you need to use .iloc to select the indices:
for train_index, validation_index in kfold.split(X, y):
# Fit the model
X_train = X.iloc[train_index]
y_train = y[train_index]
clf.fit(X_train, y_train)
I have build a random forest classifier using scikit learn and python, and I am having trouble actually feeding data in to see the prediction. I want to see the format of the output, and to convert this to a json file. I have attached the code for the random forest and what the data looks like. I believe I need to use 'y_pred', but I am not sure what format the input data needs to be.
X = dataset.iloc[:, 2:4].values
y = dataset["pages"]
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=20, random_state = 0)
classifier = classifier.fit(X_train,y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
You can simply concatenate the predicted values with the matrix of features.
Also note that the pipeline is exactly for this purpose, when you first want to transform the data and then apply some classifier.
This should work for you:
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
X = dataset.iloc[:, 2:4].values
y = dataset["pages"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
classifier = make_pipeline(StandardScaler(), RandomForestClassifier(n_estimators=20, random_state=0))
classifier = classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
pred = pd.concat([X_test, pd.Series(y_pred, name="pages")], axis=1)
i have more than 2000 data sets for ANN. I have applied MLPRegressor in it. My code is working fine. But for testing, i want to fix my testing value for instance i have 50 data sets. From that i want to test first 20 value. How do I fix this in the code? I have used the following code.
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPRegressor
df = pd.read_csv("0.5-1.csv")
df.head()
X = df[['wavelength', 'phase velocity']]
y = df['shear wave velocity']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import mean_absolute_error
mlp = MLPRegressor(hidden_layer_sizes=(30,30,30))
mlp.fit(X_train,y_train)
If you want this for reproducible results, you can pass train_test_split a fix random seed so that in each run, same train/test samples are used. The benefit if using train_test_split would be to choose train/test splits nice and clean with no further effort.
But if you insist on manually choosing train/test split like you said, you can do it this way:
X_test, y_test = X[:20], y[:20] # first 20 samples for test
X_train, y_train = X[20:], y[20:] # rest of samples for train
fix the random seed for numpy as 48 or something else
np.random.seed(48)
this will generate identical splits every time. And use testsize for fixing the size of the split
Each time when I run this code, accuracy comes out different. Can anyone please explain why? Am I missing something here ? Thanks in advance :)
Below is my code:
import scipy
import numpy
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train,y_test = train_test_split(X,y, test_size = .5)
# Use a classifier of K-nearestNeibour
from sklearn.neighbors import KNeighborsClassifier
my_classifier = KNeighborsClassifier()
my_classifier.fit(X_train,y_train)
predictions = my_classifier.predict(X_test)
print(predictions)
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test,predictions))
train_test_split randomly splits the data into training and test sets, and so you will get different splits each time you run the script. If you want, there's a random_state parameter that you can set to some number and it will ensure that you get the same split each time you run the script:
X_train, X_test, y_train,y_test = train_test_split(X,y, test_size = .5, random_state = 0)
This should give you an accuracy of 0.96 every time.
I have 4 features and one target variable. I am using RandomForestRegressor instead of RandomForestClassifer as my target variable is float. When I am trying to fit my model and then output them in sorted order to get the important features I am getting Not fitted error how to fix it?
Code:
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn import datasets
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accuracy_score
# Split the data into 30% test and 70% training
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
feat_labels = data.columns[:4]
regr = RandomForestRegressor(max_depth=2, random_state=0)
#clf = RandomForestClassifier(n_estimators=100, random_state=0)
# Train the classifier
#clf.fit(X_train, y_train)
regr.fit(X, y)
importances = clf.feature_importances_
indices = np.argsort(importances)[::-1]
for f in range(X_train.shape[1]):
print("%2d) %-*s %f" % (f + 1, 30, feat_labels[indices[f]], importances[indices[f]]))
You are fitting to regr but calling the feature importances on clf. Try calling this instead:
importances = regr.feature_importances_
I noticed that previously your classifier was being fit with the training data you setup, but the regressor is now being fit with X and y.
However, I don't see here where you're setting X and y in the first place or even more where you actually load in a dataset. Could it be you forgot this step as well as what Harpal mentioned in another answer?