ValueError: Number of features of the model must match the input

user_6490799 0
Viewed 51588

I'm getting this error when trying to predict using a model I built in scikit learn. I know that there are a bunch of questions about this but mine seems different from them because I am wildly off between my input and model features. Here is my code for training my model (FYI the .csv file has 45 columns with one being the known value): 

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import ensemble
from sklearn.metrics import mean_absolute_error
from sklearn.externals import joblib


df = pd.read_csv("Cinderella.csv")


features_df = pd.get_dummies(df, columns=['Overall_Sentiment', 'Word_1','Word_2','Word_3','Word_4','Word_5','Word_6','Word_7','Word_8','Word_9','Word_10','Word_11','Word_1','Word_12','Word_13','Word_14','Word_15','Word_16','Word_17','Word_18','Word_19','Word_20','Word_21','Word_22','Word_23','Word_24','Word_25','Word_26','Word_27','Word_28','Word_29','Word_30','Word_31','Word_32','Word_33','Word_34','Word_35','Word_36','Word_37','Word_38','Word_39','Word_40','Word_41', 'Word_42', 'Word_43'], dummy_na=True)

del features_df['Slope']

X = features_df.as_matrix()
y = df['Slope'].as_matrix()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

model = ensemble.GradientBoostingRegressor(
    n_estimators=500,
    learning_rate=0.01,
    max_depth=5,
    min_samples_leaf=3,
    max_features=0.1,
    loss='lad'
)

model.fit(X_train, y_train)

joblib.dump(model, 'slope_from_sentiment_model.pkl')

mse = mean_absolute_error(y_train, model.predict(X_train))

print("Training Set Mean Absolute Error: %.4f" % mse)

mse = mean_absolute_error(y_test, model.predict(X_test))
print("Test Set Mean Absolute Error: %.4f" % mse)

Here is my code for the actual prediction using a different .csv file (this has 44 columns because it doesn't have any values): 

from sklearn.externals import joblib
import pandas


model = joblib.load('slope_from_sentiment_model.pkl')

df = pandas.read_csv("Slaughterhouse_copy.csv")


features_df = pandas.get_dummies(df, columns=['Overall_Sentiment','Word_1', 'Word_2', 'Word_3', 'Word_4', 'Word_5', 'Word_6', 'Word_7', 'Word_8', 'Word_9', 'Word_10', 'Word_11', 'Word_12', 'Word_13', 'Word_14', 'Word_15', 'Word_16', 'Word_17','Word_18','Word_19','Word_20','Word_21','Word_22','Word_23','Word_24','Word_25','Word_26','Word_27','Word_28','Word_29','Word_30','Word_31','Word_32','Word_33','Word_34','Word_35','Word_36','Word_37','Word_38','Word_39','Word_40','Word_41','Word_42','Word_43'], dummy_na=True)

predicted_slopes = model.predict(features_df)

When I run the prediction file I get: 

ValueError: Number of features of the model must match the input. Model n_features is 146 and input n_features is 226.

If anyone could help me it would be greatly appreciated! Thanks in advance!

scikit-learn csv python
5 Answers

Below correction to original answer from Scratch'N'Purr would help solve issues one might face using string as value for new inserted column 'label' -
train_df = pd.read_csv("Cinderella.csv") train_df['label'] = 1

    score_df = pandas.read_csv("Slaughterhouse_copy.csv")
    score_df['label'] = 2

    # Concat
    concat_df = pd.concat([train_df , score_df])

    # Create your dummies
    features_df = pd.get_dummies(concat_df)

    # Split your data
    train_df = features_df[features_df['label'] == '1]
    score_df = features_df[features_df['label'] == '2]
    ...
user_111947490

The size of the training data(excluding labels,however) which you fit to the model should be same as the size of the data which you are going to predict

user_132254480

You can utilize the Categorical Dtype to apply null values to unseen data.

Input:

import pandas as pd
import numpy as np
from pandas.api.types import CategoricalDtype

# Create Example Data
train = pd.DataFrame({"text":["A", "B", "C", "D", 'F', np.nan]})
test = pd.DataFrame({"text":["D", "D", np.nan,"B", "E", "T"]})

# Convert columns to category dtype and specify categories for test set
train['text'] = train['text'].astype('category')
test['text'] = test['text'].astype(CategoricalDtype(categories=train['text'].cat.categories))

# Create Dummies
pd.get_dummies(test['text'], dummy_na=True)

Output:

| A | B | C | D | F | nan |
|---|---|---|---|---|-----|
| 0 | 0 | 0 | 1 | 0 | 0   |
| 0 | 0 | 0 | 1 | 0 | 0   |
| 0 | 0 | 0 | 0 | 0 | 1   |
| 0 | 1 | 0 | 0 | 0 | 0   |
| 0 | 0 | 0 | 0 | 0 | 1   |
| 0 | 0 | 0 | 0 | 0 | 1   |
user_38666780
Related