I have a dataset that shows whether a person has diabetes based on indicators, it looks like this (original dataset):
I've created a straightforward model in order to predict the last column (Outcome).
#Libraries imported
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
#Dataset imported
data = pd.read_csv('diabetes.csv')
#Assign X and y
X = data.iloc[:,:-1].values
y = data.iloc[:,-1].values
#Data preprocessed
sc = StandardScaler()
X = sc.fit_transform(X)
#Dataset split between train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
# Predicting the results for the whole dataset
y_pred2 = model.predict(data)
#Add prediction column to original dataset
data['prediction'] = y_pred2
However, I get the following error: ValueError: X has 9 features per sample; expecting 8.
My questions are:
Why can't I create a new column with the predictions for my entire dataset?
How can I make predictions for blank outcomes (that need to be predicted), that is to say, should I upload the file again? Let's say I want to predict the folowing:
Rows to predict:
Please let me know if my questions are clear!
You are feeding data (with all 9 initial features) to a model that was trained with X (8 features, since Outcome has been removed to create y), hence the error.
What you need to do is:
Get predictions using X instead of data
Append the predictions to your initial data set
i.e.:
y_pred2 = model.predict(X)
data['prediction'] = y_pred2
Keep in mind that this means that your prediction variable will come from both data that have already been used for model fitting (i.e. the X_train part) as well as from data unseen by the model during training (the X_test part). Not quite sure what your final objective is (and neither this is what the question is about), but this is a rather unusual situation from an ML point of view.
If you have a new dataset data_new to predict the outcome, you do it in a similar way; always assuming that X_new has the same features with X (i.e. again removing the Outcome column as you have done with X):
y_new = model.predict(X_new)
data_new['prediction'] = y_new
Related
I am currently using the scikit learn module in order to help with a crime prediction problem. I am having an issue batch coding the entire Dataframe that I have with the knn.predict method.
How can I batch code the entire two columns of my Dataframe with the knn.predict() method in order to store in another Dataframe the output?
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
knn_df = pd.read_csv("/Users/helenapunset/Desktop/knn_dataframe.csv")
# x is the set of features
x = knn_df[['latitude', 'longitude']]
# y is the target variable
y = knn_df['Class']
# train and test data
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5)
# training the data
knn.fit(x_train,y_train)
# test score was approximately 69%
knn.score(x_test,y_test)
# this is predicted to be a safe zone
crime_prediction = knn.predict([[25.787882, -80.358427]])
print(crime_prediction)
In the last line of the code I was able to add the two features I am using which are latitude and longitude from my Dataframe labeled knn_df. But, this is a single point I have been searching through the documentation on a process for streamlining this knn prediction for the entire Dataframe and cannot seem to find a way to do this. Is there somehow a possibility of using a for loop for this?
Let the new set to be predicted is 'knn_df_predict'. Assuming same column names,try the following lines of code :
x_new = knn_df_predict[['latitude', 'longitude']] #formating features
crime_prediction = knn.predict(x_new) #predicting for the new set
knn_df_predict['prediction'] = crime_prediction #Adding the prediction to dataframe
I have a dataframe with 36540 rows. the objective is to predict y HITS_DAY.
#data
https://github.com/soufMiashs/Predict_Hits
I am trying to train a non-linear regression model but model doesn't seem to learn much.
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
data_dmatrix = xgb.DMatrix(data=x,label=y)
xg_reg = xgb.XGBRegressor(learning_rate = 0.1, objectif='reg:linear', max_depth=5,
n_estimators = 1000)
xg_reg.fit(X_train,y_train)
preds = xg_reg.predict(X_test)
df=pd.DataFrame({'ACTUAL':y_test, 'PREDICTED':preds})
what am I doing wrong?
You're not doing anything wrong in particular (except maybe the objectif parameter for xgboost which doesn't exist), however, you have to consider how xgboost works. It will try to create "trees". Trees have splits based on the values of the features. From the plot you show here, it looks like there are very few samples that go above 0. So making a test train split random will likely result in a test set with virtually no samples with a value above 0 (so a horizontal line).
Other than that, it seems you want to fit a linear model on non-linear data. Selecting a different objective function is likely to help with this.
Finally, how do you know that your model is not learning anything? I don't see any evaluation metrics to confirm this. Try to think of meaningful evaluation metrics for your model and show them. This will help you determine if your model is "good enough".
To summarize:
Fix the imbalance in your dataset (or at least take it into consideration)
Select an appropriate objective function
Check evaluation metrics that make sense for your model
From this example it looks like your model is indeed learning something, even without parameter tuning (which you should do!).
import pandas
import xgboost
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
# Read the data
df = pandas.read_excel("./data.xlsx")
# Split in X and y
X = df.drop(columns=["HITS_DAY"])
y = df["HITS_DAY"]
# Show the values of the full dataset in a plot
y.sort_values().reset_index()["HITS_DAY"].plot()
# Split in test and train, use stratification to make sure the 2 groups look similar
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.20, random_state=42, stratify=[element > 1 for element in y.values]
)
# Show the plots of the test and train set (make sure they look similar!)
y_train.sort_values().reset_index()["HITS_DAY"].plot()
y_test.sort_values().reset_index()["HITS_DAY"].plot()
# Create the regressor
estimator = xgboost.XGBRegressor(objective="reg:squaredlogerror")
# Fit the regressor
estimator.fit(X_train, y_train)
# Predict on the test set
predictions = estimator.predict(X_test)
df = pandas.DataFrame({"ACTUAL": y_test, "PREDICTED": predictions})
# Show the actual vs predicted
df.sort_values("ACTUAL").reset_index()[["ACTUAL", "PREDICTED"]].plot()
# Show some evaluation metrics
print(f"Mean squared error: {mean_squared_error(y_test.values, predictions)}")
print(f"R2 score: {r2_score(y_test.values, predictions)}")
Output:
Mean squared error: 0.01525351142868279
R2 score: 0.07857787102063485
My issue is similar to this question. But I didn't get the answer. I need further clarification.
I am using sklearn linear regression prediction -for the first time- to add more data points to my dataset. Adding more data points will help me identify outliers more accurately. I have built my model and got the predictions but I want the model to return predicted points with a certain range. Is it possible to achieve this?
I would like to predict values in a column called 'delivery_fee'.
The values in the column starts from 3 and increases steadily until it reaches 27.
The last value in the column and it comes right after 27 is 47.
I would like the model to predict values between 27 and 47.
my code:
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing
#create a copy of the dataframe
delivery_linreg = outlierFileNew.copy()
le = preprocessing.LabelEncoder()
delivery_linreg['branch_code'] = le.fit_transform(delivery_linreg['branch_code'])
#select all columns in the datframe except for delivery_fee
x = delivery_linreg[[x for x in delivery_linreg.columns if x != 'delivery_fee']]
#selecting delivery_fee as the column to be predicted
y = delivery_linreg.delivery_fee
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)
#fitting simple linear regression to training set
linreg = LinearRegression()
linreg.fit(x_train,y_train)
delivery_predict = linreg.predict(x_test)
My model returns values that range from 4 to 17. Which is not the range I want. Any suggestions on how to change the predicted range?
Thank you,
I'm trying to train a decision tree classifier using Python. I'm using MinMaxScaler() to scale the data, and f1_score for my evaluation metric. The strange thing is that I'm noticing my model giving me different results in a pattern at each run.
data in my code is a (2000, 7) pandas.DataFrame, with 6 feature columns and the last column being the target value. Columns 1, 3, and 5 are categorical data.
The following code is what I did to preprocess and format my data:
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import f1_score
# Data Preprocessing Step
# =============================================================================
data = pd.read_csv("./data/train.csv")
X = data.iloc[:, :-1]
y = data.iloc[:, 6]
# Choose which columns are categorical data, and convert them to numeric data.
labelenc = LabelEncoder()
categorical_data = list(data.select_dtypes(include='object').columns)
for i in range(len(categorical_data)):
X[categorical_data[i]] = labelenc.fit_transform(X[categorical_data[i]])
# Convert categorical numeric data to one-of-K data, and change y from Series to ndarray.
onehotenc = OneHotEncoder()
X = onehotenc.fit_transform(X).toarray()
y = y.values
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)
min_max_scaler = MinMaxScaler()
X_train_scaled = min_max_scaler.fit_transform(X_train)
X_val_scaled = min_max_scaler.fit_transform(X_val)
The next code is for the actual decision tree model training:
dectree = DecisionTreeClassifier(class_weight='balanced')
dectree = dectree.fit(X_train_scaled, y_train)
predictions = dectree.predict(X_val_scaled)
score = f1_score(y_val, predictions, average='macro')
print("Score is = {}".format(score))
The output that I get (i.e. the score) varies, but in a pattern. For example, it would circulate among data within the range of 0.39 and 0.42.
On some iterations, I even get the UndefinedMetricWarning, that claims "F-score is ill-defined and being set to 0.0 in labels with no predicted samples."
I'm familiar with what the UndefinedMetricWarning means, after doing some searching on this community and Google. I guess the two questions I have may be organized to:
Why does my output vary for each iteration? Is there something in the preprocessing stage that happens which I'm not aware of?
I've also tried to use the F-score with other data splits, but I always get the warning. Is this unpreventable?
Thank you.
You are splitting the dataset into train and test which randomly divides sets for both train and test. Due to this, when you train your model with different training data everytime, and testing it with different test data, you will get a range of F score depending on how well the model is trained.
In order to replicate the result each time you run, use random_state parameter. It will maintain a random number state which will give you the same random number each time you run. This shows that the random numbers are generated in the same order. This can be any number.
#train test split
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=13)
#Decision tree model
dectree = DecisionTreeClassifier(class_weight='balanced', random_state=2018)
I'm trying to learn scikit-learn and Machine Learning by using the Boston Housing Data Set.
# I splitted the initial dataset ('housing_X' and 'housing_y')
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(housing_X, housing_y, test_size=0.25, random_state=33)
# I scaled those two datasets
from sklearn.preprocessing import StandardScaler
scalerX = StandardScaler().fit(X_train)
scalery = StandardScaler().fit(y_train)
X_train = scalerX.transform(X_train)
y_train = scalery.transform(y_train)
X_test = scalerX.transform(X_test)
y_test = scalery.transform(y_test)
# I created the model
from sklearn import linear_model
clf_sgd = linear_model.SGDRegressor(loss='squared_loss', penalty=None, random_state=42)
train_and_evaluate(clf_sgd,X_train,y_train)
Based on this new model clf_sgd, I am trying to predict the y based on the first instance of X_train.
X_new_scaled = X_train[0]
print (X_new_scaled)
y_new = clf_sgd.predict(X_new_scaled)
print (y_new)
However, the result is quite odd for me (1.34032174, instead of 20-30, the range of the price of the houses)
[-0.32076092 0.35553428 -1.00966618 -0.28784917 0.87716097 1.28834383
0.4759489 -0.83034371 -0.47659648 -0.81061061 -2.49222645 0.35062335
-0.39859013]
[ 1.34032174]
I guess that this 1.34032174 value should be scaled back, but I am trying to figure out how to do it with no success. Any tip is welcome. Thank you very much.
You can use inverse_transform using your scalery object:
y_new_inverse = scalery.inverse_transform(y_new)
Bit late to the game:
Just don't scale your y. With scaling y you actually loose your units. The regression or loss optimization is actually determined by the relative differences between the features. BTW for house prices (or any other monetary value) it is common practice to take the logarithm. Then you obviously need to do an numpy.exp() to get back to the actual dollars/euros/yens...