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1

An error occurred while loading the model: node array from the pickle has an incompatible dtype:

  • expected: {‘names’: [‘left_child’, ‘right_child’, ‘feature’, ‘threshold’, ‘impurity’, ‘n_node_samples’, ‘weighted_n_node_samples’, ‘missing_go_to_left’], ‘formats’: [‘<i8’, ‘<i8’, ‘<i8’, ‘<f8’, ‘<f8’, ‘<i8’, ‘<f8’, ‘u1’], ‘offsets’: [0, 8, 16, 24, 32, 40, 48, 56], ‘itemsize’: 64}
  • got : [(‘left_child’, ‘<i8’), (‘right_child’, ‘<i8’), (‘feature’, ‘<i8’), (‘threshold’, ‘<f8’), (‘impurity’, ‘<f8’), (‘n_node_samples’, ‘<i8’), (‘weighted_n_node_samples’, ‘<f8’)]

this is the error ; how to solve this

2

Hello

Maybe, you may need to manually convert the loaded model into the expected format. You can iterate over the loaded data and construct a new data structure that matches the expected format.

3

Can you show an example to do this

(I’ve also changed scikit-learn version as 1.2.2 , but the error still exists)

4

Here’s an example of how to save and load a scikit-learn model using joblib, which is a commonly used library for serialization:

import numpy as np
import joblib
import pandas as pd
import streamlit as st

# Sample data
data = {
    'left_child': [0, 0, 2, -1, -1, 5, -1, -1, 8, -1, -1],
    'right_child': [1, 4, 3, -1, -1, 6, 7, -1, 9, 10, -1],
    'feature': [2, 3, 2, -2, -2, 3, -2, -2, 3, -2, -2],
    'threshold': [0.800000011920929, 1.75, 4.949999809265137, -2.0, -2.0, 1.6500000953674316, -2.0, -2.0, 1.5499999523162842, -2.0, -2.0],
    'impurity': [0.6666666666666666, 0.6666666666666666, 0.5, -2.0, -2.0, 0.1680384087791495, -2.0, -2.0, 0.4444444444444444, -2.0, -2.0],
    'n_node_samples': [3, 2, 1, -2, -2, 4, -2, -2, 3, -2, -2],
    'weighted_n_node_samples': [3.0, 2.0, 1.0, -2.0, -2.0, 4.0, -2.0, -2.0, 3.0, -2.0, -2.0],
    'missing_go_to_left': [1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0]
}

# Define the expected data structure
expected_dtype = np.dtype({
    'names': ['left_child', 'right_child', 'feature', 'threshold', 'impurity', 'n_node_samples', 'weighted_n_node_samples', 'missing_go_to_left'],
    'formats': ['<i8', '<i8', '<i8', '<f8', '<f8', '<i8', '<f8', 'u1'],
    'offsets': [0, 8, 16, 24, 32, 40, 48, 56],
    'itemsize': 64
})

# Load the data
loaded_data = np.array(list(zip(*[data[key] for key in expected_dtype.names])), dtype=expected_dtype)

# Save the loaded data
joblib.dump(loaded_data, 'loaded_data.pkl')

# Load the data
reloaded_data = joblib.load('loaded_data.pkl')

# Convert the numpy array to a pandas DataFrame for easier visualization
df = pd.DataFrame(loaded_data)

# Display the DataFrame in Streamlit
st.write(df)
5

As you given an example to solve the problem but.its not working on my project. Can you help me to do this

6

check if you have installed the libraries

7

import streamlit as st
import numpy as np
from PIL import Image
import pickle

Attempt to load the random forest classifier model

rfc = None
try:
with open(‘rfc.pkl’, ‘rb’) as file:
rfc = pickle.load(file)
except FileNotFoundError:
st.error(“The file ‘rfc.pkl’ was not found.”)
except Exception as e:
st.error(f"An error occurred while loading the model: {e}")

Creating the web app

st.title(‘Forest Cover Type Prediction’)

try:
image = Image.open(‘img.jpeg’)
st.image(image, caption=‘myimage’, use_column_width=True)
except FileNotFoundError:
st.error(“Error: The image file ‘img.jpeg’ was not found.”)

user_input = st.text_input(‘Input Features’)

if user_input and rfc:
try:
user_input = user_input.split(‘,’)
features = np.array([user_input], dtype=np.float64)
output = rfc.predict(features).reshape(-1) # Remove unnecessary reshape

    # Create the cover type dictionary with corrected image file names
    cover_type_dict = {
        1: {"name": "Spruce/Fir", "image": "Spruce_Fir.jpg"},
        2: {"name": "Lodgepole Pine", "image": "Lodgepole_Pine.jpg"},
        3: {"name": "Ponderosa Pine", "image": "Ponderosa.jpg"},
        4: {"name": "Cottonwood/Willow", "image": "Cottonwood_Willow.jpg"},
        5: {"name": "Aspen", "image": "Aspen.jpg"},
        6: {"name": "Douglas-fir", "image": "Douglas-fir.jpg"},
        7: {"name": "Krummholz", "image": "Krummholz.jpg"}
    }

    predicted_cover_type = output[0]  # No need to convert to int
    cover_type_info = cover_type_dict.get(predicted_cover_type)

    if cover_type_info:
        col1, col2 = st.columns([2, 3])

        with col1:
            st.write("Predicted Cover Type:")
            st.markdown(f"<h1 style='font-size: 40px; font-weight: bold;'>{cover_type_info['name']}</h1>", unsafe_allow_html=True)

        with col2:
            cover_type_image = Image.open(cover_type_info["image"])
            st.image(cover_type_image, caption=cover_type_info['name'], use_column_width=True)
except Exception as e:
    st.error(f"An error occurred during prediction: {e}")

elif rfc is None:
st.write(“Model not loaded. Unable to make predictions.”)

This is my program code

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